JP2003132344A - Image processing method and image processing apparatus, program, recording medium, and image forming apparatus - Google Patents

Abstract

(57) Abstract: To convert an input image into a visually preferable image, a type of noise to be superimposed on the input image and a strength of the noise are determined, and a superimposition process for superimposing the noise on the input image is performed. Provided are an image processing method and an image processing apparatus, a program, a recording medium, and an image forming apparatus capable of smoothly performing the processing. An analysis step for obtaining a fractal dimension of an input image, and a type and a noise intensity of a noise to be superimposed on the input image for converting the input image into a predetermined image are determined based on the obtained fractal dimension. And a noise parameter setting step. This makes it possible to smoothly determine the type of noise adapted to the complexity of the input image without trial and error to determine the type and intensity of the noise to be superimposed on the input image. By superimposing the noise of the type and the strength determined in this way on the input image, the input image can be converted into a visually preferable image.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing method for converting an input image into a visually pleasing image by superimposing noise such that a predetermined visual effect such as giving a texture to the input image can be obtained. The present invention relates to an image processing device, a program and a recording medium, and an image forming device.

[0002]

2. Description of the Related Art In recent years, with the rapid progress of digitization of office equipment, computers have spread to the individual level. With the widespread use of such computers, it becomes easy to perform various types of images by using a computer to perform image processing on an image captured by a digital camera and an image read by an image input device such as a scanner device. Came.

Noise superposition processing is one of image processing for giving a texture to an image created by using a computer so as to make the image more realistic. Noise superimposition processing is performed on the area selected in the image by the computer operator.
This is a process of giving a texture to an image by selecting and superimposing noise according to the operator's request from a plurality of types of noise samples prepared in advance.

[0004]

In such a noise superimposing process, the operator must make a trial and error while observing the image on which the noise is superimposed to determine what kind of noise is superposed and at what strength. Therefore, the operator needs to be familiar with the nature of noise such as the type of noise to be superimposed and the intensity of noise, and an operator unfamiliar with image processing should superimpose the desired noise on the image. Is
It's extremely difficult.

Therefore, in order to convert an input image into a visually preferable image, an object of the present invention is to determine the type of noise to be superimposed on the input image and the strength of the noise, and to superimpose the noise on the input image. An object of the present invention is to provide an image processing method, an image processing apparatus, a program, a recording medium, and an image forming apparatus that can smoothly perform processing.

[0006]

The present invention includes an analysis step for determining a fractal dimension of an input image and a type determination step for determining the type of noise to be superimposed on the input image based on the determined fractal dimension. Is an image processing method.

According to the present invention, in the analyzing step, the fractal dimension of the input image is obtained, and in the type determining step, the type of noise to be superimposed on the input image is determined based on the fractal dimension. Since the fractal dimension quantitatively represents the complexity of the input image, it is possible to smoothly determine the type of noise that adapts to the complexity of the input image without trial and error to determine the type of noise to be superimposed on the input image. can do.
By superimposing the type of noise thus determined on the input image, the input image can be converted into a visually preferable image.

Further, the present invention is characterized in that, after the type determining step, an intensity determining step of determining the intensity of noise indicating the degree of conversion by the noise with respect to the input image is included based on the fractal dimension.

According to the present invention, in the intensity determining step performed after the type determining step, the intensity of noise indicating the degree of conversion of the input image due to noise is determined based on the fractal dimension. This makes it possible to smoothly determine the noise intensity that adapts to the complexity of the input image without trial and error for determining the noise intensity that is superimposed on the input image. By superimposing noise on the input image with the strength thus determined, the input image can be converted into a visually preferable image.

Further, according to the present invention, after the analysis step, the necessity of the superimposing process for superimposing noise on the input image is determined based on the fractal dimension, and whether or not the superimposing process is performed is determined based on the necessity. It is characterized by including a process determining step of determining.

According to the present invention, in the process determining process performed after the analyzing process, the necessity of the superimposing process for superimposing noise on the input image is determined based on the fractal dimension, and the superimposing process is performed based on the necessity. Decide whether to do or not. As a result, when the necessity of the superimposing process is high, in other words, when it is preferable to superpose the noise on the input image, the superimposing process is performed. When the necessity of the superimposing process is low, in other words, the noise is added to the input image. If it is preferable not to superpose, the superimposing process is not performed, so that the input image can be surely converted into a visually preferable image.

If the process determination step is after the analysis step,
The order of the type determining process and the intensity determining process is not limited, but the process determining process may be performed before the type determining process in order to reduce the number of processes until the superimposing process. Further, in such a process determining step, the user may be notified of the necessity degree of the superimposing process, and the user may be made to input whether or not to perform the superimposing process, or a threshold value may be set for the necessity degree. Then, it may be determined whether or not to perform the superimposing process depending on whether or not the necessity degree exceeds the threshold value.

The present invention is also a program for causing a computer to execute the above-described image processing method.

According to the present invention, the computer can be made to execute the above-mentioned image processing. The computer obtains the fractal dimension of the input image in the analysis step, and automatically determines the type of noise to be superimposed on the input image in order to convert the input image into a predetermined image based on the fractal dimension in the type determination step. To do. In addition, the computer, in the strength determination process performed after the type determination process,
Based on the fractal dimension, the strength of noise that indicates the degree of conversion by the noise with respect to the input image is automatically determined. Since the fractal dimension quantitatively represents the complexity of the input image, the noise that adapts to the complexity of the input image can be determined without trial and error to determine the type of noise and noise intensity to be superimposed on the input image. The type and the strength of noise can be determined automatically and smoothly. By superimposing the noise of the type and strength thus determined on the input image, the input image can be converted into a visually preferable image.

Further, the computer, in the process determining step performed after the analyzing step, based on the fractal dimension,
The necessity of the superimposing process for superimposing noise on the input image is determined, and based on the necessity, whether or not to perform the superimposing process is determined. As a result, when the necessity of the superimposing process is high, in other words, when it is preferable to superpose the noise on the input image, the superimposing process is performed. When the necessity of the superimposing process is low, in other words, the noise is added to the input image. If it is preferable not to superpose, the superimposing process is not performed, so that the input image can be surely converted into a visually preferable image.

Further, the present invention is a computer-readable recording medium in which the above program is recorded.

According to the present invention, it is possible to cause the computer to read and execute the recorded program to execute the above-described image processing method. Further, the program can be easily supplied to a plurality of computers via the recording medium.

The present invention also provides an analyzing means for determining the fractal dimension of the input image, a type determining means for determining the type of noise to be superimposed on the input image based on the fractal dimension obtained by the analyzing means, and a type determining means. An image processing apparatus comprising: a superposition processing unit that superimposes noise of a type determined by the above on an input image.

According to the present invention, the analyzing means determines the fractal dimension of the input image, and the type determining means determines the type of noise to be superimposed on the input image based on the fractal dimension obtained by the analyzing means, The superposition processing means superimposes the type of noise determined by the type determination means on the input image. The fractal dimension quantitatively represents the complexity of the input image, so that the type of noise that adapts to the complexity of the input image is automatically and automatically determined without trial and error to determine the type of noise to be superimposed on the input image. It can be decided smoothly. By superimposing the type of noise thus determined on the input image, the input image can be converted into a visually preferable image.

The present invention further includes intensity determining means for determining the intensity of noise indicating the degree of conversion by the noise with respect to the input image based on the fractal dimension, and the superimposing processing means includes the noise determined by the intensity determining means. Is characterized in that noise is superimposed on the input image with the strength of.

According to the present invention, the intensity determining means determines the intensity of noise indicating the degree of conversion by the noise with respect to the input image based on the fractal dimension, and the superposition processing means is determined by the intensity determining means. Noise is superimposed on the input image according to the strength of the noise. This makes it possible to automatically and smoothly determine the noise intensity that adapts to the complexity of the input image, without trial and error to determine the intensity of noise that is superimposed on the input image. By superimposing noise on the input image with the strength thus determined, the input image can be converted into a visually preferable image.

Further, the present invention includes a judging means for deciding the necessity of the superimposing processing for superposing noise on the input image based on the fractal dimension, and displaying the necessity of the superimposing processing decided by the judging means on the display means. However, when the processing information for executing the superimposition processing is input by the input means for inputting the processing information as to whether or not to execute the superposition processing, the superposition processing means is characterized by performing the superposition processing.

According to the invention, the determining means determines the necessity of the superimposing processing for superimposing noise on the input image based on the fractal dimension, and the necessity of the superimposing processing determined by the determining means is displayed on the display means. Is displayed. The processing information indicating whether or not to execute the superimposition processing is input from the input unit, and the superposition processing unit performs the superposition processing when the processing information for executing the superposition processing is input by the input unit. With this, the user can select whether or not to perform the superimposing process according to the necessity of the superimposing process displayed on the display unit and input the process information to the input unit.

Further, the present invention is an image forming apparatus including the above-mentioned image processing apparatus.

According to the present invention, it is possible to realize an image forming apparatus capable of achieving the above-described operation. As a result, the input image can be converted into a visually preferable image and the image can be formed on the recording paper.

[0026]

1 is a flowchart showing a main routine of a procedure of an image processing method according to an embodiment of the present invention. The image processing method is a method for converting an input image into a visually preferable image by superimposing noise that gives a predetermined visual effect such as giving a texture to the input image. In step s0, the procedure of the image processing method is started, and the order of the monochrome grayscale image conversion step of step s1, the analysis step of step s2, the noise parameter setting step of step s3, the noise generation step of step s4, and the noise superposition step of step s5. The process proceeds in step S6, and all procedures are completed in step s6.

In the black-and-white gradation image conversion step of step s1, if the input image is a color image, the RGB (red, green, blue) components of the density of each pixel of the input image are calculated using the following equation (1). It is converted into one luminance and the input image is converted into a black and white grayscale image to be an analysis image for obtaining a fractal dimension in an analysis step described later. Y _i = 0.30R _i + 0.59G _i + 0.11B _i (1)

In the above equation (1), Y _i represents the luminance of the i-th pixel of the input image converted into the monochrome grayscale image,
R _i represents the red density of the i-th pixel of the input image,
G _i represents the green density of the i-th pixel of the input image,
B _i represents the blue density of the i-th pixel of the input image.
For example, in a 24-bit color image, R _i , G _i , B _i
Respectively take a value of 0 or more and 255 or less, and at this time Y _i
Takes a value of 0 or more and 255 or less. If the input image is a binary image or a grayscale image, no conversion is performed,
The input image is directly used as the analysis image.

FIG. 2 is a flow chart showing a subroutine of the analysis process of step s2 in the main routine of the procedure of the image processing method of FIG. In the analysis step of step s2 in the main routine of the procedure of the image processing method, the fractal dimension of the input image is obtained. The fractal dimension is a numerical value that quantitatively represents the self-similarity and complexity of a figure, and the larger the value, the more complicated the figure. For example, the fractal dimension of a line figure takes a real value of 1.0 or more and less than 2.0, and the fractal dimension of a monochrome grayscale image takes a real number of 2.0 or more and less than 3.0. In this embodiment, the fractal dimension of the input image is obtained by the box count method. Further, in the present embodiment, the input image is an image of P × Q pixels in which P pixels are arranged in the horizontal direction and Q pixels are arranged in the vertical direction.

FIG. 3 is a diagram showing an example of the analysis image G1 which is a binary image. First, the analysis process when the analysis image G1 is a binary image in which a black curve L as shown in FIG. 3 is drawn will be described. The procedure of the analysis process shown in FIG. 2 starts at step a0 and proceeds to step a1.

FIG. 4 is a diagram showing a grid-divided analysis image G1. At step a1, an initial value of the grid interval r for dividing the analysis image G1 into grids is set, and the process proceeds to step a2. In the present embodiment, the analysis image G1 is divided in the vertical direction and the horizontal direction at the grid spacing r, and the initial value of the grid spacing r is, for example, the pixel spacing Δ of the analysis image G1.

In step a2, the number N of the boxes B1 including the black curve L in the square region (box) of one side length r which is grid-divided in the analysis image G1.
(R) is counted and the process proceeds to step a3. N (r) is 13 in the analysis image G1 that is grid-divided at the grid interval r shown in FIG.

At step a3, the value 2r obtained by multiplying the lattice spacing r by 2 is the smaller value min (P, P of the number of pixels P in the horizontal direction and the number of pixels Q in the vertical direction of the image for analysis G1).
It is determined whether or not it is less than Q), and 2r is min (P,
If it is less than Q), the process proceeds to step a4 and 2r is m.
If it is greater than or equal to in (P, Q), the process proceeds to step a5.

In step a3, 2r is min
When it is determined that the value is less than (P, Q) and the process proceeds to step a4, at step a4, a value 2r obtained by multiplying the lattice interval r by 2 is obtained.
Is set as a new lattice spacing r, and the process returns to step a2. In this way, the lattice spacing r is set to Δ, 2Δ, 4Δ, 8Δ,
While increasing to 16Δ, ..., EΔ, 2r is min
Until it is determined that (P, Q) or more, step a2
~ Repeat step a4. The lattice spacing r is expressed by the following equation (2).

[0035]

[Equation 1]

In the above equation (2), [] represents a Gauss symbol. The Gauss symbol is [e] for some real number e.
Indicates the maximum integer not exceeding e.

FIG. 5 is a graph showing the relationship between log r and log N (r) and the regression line. Step a3
In 2a, it is determined that 2r is equal to or greater than min (P, Q), and the process proceeds to step a5. At step a5, the abscissa indicates logr and the ordinate indicates log N for each lattice spacing r.
(R), and coordinates (log r, log N (r))
Are plotted as shown in FIG. 5, and the process proceeds to step a6.

At step a6, a regression line is obtained by the method of least squares with respect to the coordinates (log r, log N (r)) plotted at step a5, and the slope of the regression line is derived. In the box count method, a fractal dimension Dim called a box dimension is defined as shown in the following expression (3).

[0039]

[Equation 2]

That is, to obtain the slope of the regression line,
This is the same as obtaining the fractal dimension Dim. When the fractal dimension Dim is obtained in this way in step a6, the process proceeds to step a7, the procedure of the analysis process is ended, and the process returns to the main routine of the image processing method shown in FIG.

FIG. 6 shows an image space S containing the analysis image G2.
FIG. Next, an analysis is performed when the input image is a monochrome grayscale image and this input image is the analysis image G2, and when the input image is converted to the analysis image G2 which is a monochrome grayscale image in step s1 of the main routine. The process will be described. The procedure of the analysis process shown in FIG. 2 starts at step a0 and proceeds to step a1.

At step a1, an initial value of a grid interval r for dividing the image space S containing the image for analysis G2 into grids is set, and the routine proceeds to step a2. The image space S is the analysis image G.
2, the x-axis indicating the horizontal direction and the y-axis indicating the vertical direction of the analysis image G2, and the luminance axis Y indicating the luminance of each pixel of the analysis image G2 in one direction perpendicular to the analysis image G2. It is a space formed as three axes. In the image space S, there is an image brightness plane D formed by connecting the brightness of each pixel of the analysis image G2. In the present embodiment, the image space is divided in the x-axis direction, the y-axis direction, and the luminance axis Y direction at the grid spacing r, and the initial value of the grid spacing r is, for example, the pixel spacing Δ of the analysis image G2. .

In step a2, the number N of the boxes B2 including the image luminance plane D in the cubic area (box) having a side length of r and divided into a lattice in the image space S.
(R) is counted and the process proceeds to step a3. In order to count the number N (r) of the boxes B2 including the image brightness plane D, it is necessary to check the intersection situation between the image brightness plane D and the box B2, but such a three-dimensional search requires a long calculation time. Therefore, "The Institute of Electronics, Information and Communication Engineers, Vol.J83-D-II
No.4 pp.1082-1089 (April 2000) ”is used to estimate N (r). This makes 3
The dimension search can be performed as a two-dimensional process, and the calculation time can be shortened. The details will be described below.

First, a square area having a side length r in the analysis image G2 when the image space S is divided into lattices as shown in FIG. 6 is defined as a unit area A. An estimated value n (r) of the number of boxes B2 existing in the luminance axis Y direction of a certain unit area A and including the image luminance plane D is obtained by the following equation (4).

[0045]

[Equation 3]

In the above equation (4), Y ₁ , Y ₂ , Y ₃ , Y ₄
Are four corners C of the unit area A existing in the unit area A.
Represents the luminance of each pixel including 1, C2, C3, C4, and ma
xY _j represents the maximum value of the luminances Y _{1 to} Y ₄ , and mi
nY _j represents the minimum value of the luminances Y _{1 to} Y ₄ ,
[] Represents a Gauss symbol.

In all the unit areas in the analysis image G2, n (r) is calculated as described above, and the average value n _ave (r) is calculated. At this time, the number N (r) of the boxes B2 including the image brightness surface D is obtained by the following equation (5).

[0048]

[Equation 4]

In the above equation (5), S _D is the area of the image brightness plane D. By calculating N (r) in this way, the calculation time for calculating N (r) can be shortened.

Then, in step a3, the lattice spacing r is set to 2
The value 2r multiplied by is the number of pixels P in the horizontal direction of the analysis image G2.
And the vertical direction pixel number Q, whichever is smaller, min
It is determined whether or not it is less than (P, Q), and 2r is min.
If it is less than (P, Q), the process proceeds to step a4, and 2
If r is more than min (P, Q), step a5
Proceed to.

In step a3, 2r is min
When it is determined that the value is less than (P, Q) and the process proceeds to step a4, at step a4, a value 2r obtained by multiplying the lattice interval r by 2 is obtained.
Is set as a new lattice spacing r, and the process returns to step a2. In this way, the lattice spacing r can be expressed by the following equation (2).
While increasing Δ, 2Δ, 4Δ, 8Δ, 16Δ, ..., EΔ, step a2 to step a4 are repeated until 2r is determined to be min (P, Q) or more.

In step a3, 2r is min
When it is determined that the value is (P, Q) or more and the process proceeds to step a5, in step a5, the abscissa indicates l for each lattice interval r.
The vertical axis is log N (r), and the coordinate (log
r, log N (r)) is plotted as shown in FIG. 5, and the process proceeds to step a6.

In step a6, a regression line is obtained by the method of least squares with respect to the coordinates (log r, log N (r)) plotted in step a5, and the fractal dimension Dim is obtained from the slope of the regression line. Then, the procedure of the analysis process is completed, and the process returns to the main routine of the image processing method shown in FIG.

FIG. 7 is a flowchart showing a subroutine of the noise parameter setting step of step s3 in the main routine of the procedure of the image processing method of FIG. Step s in the main routine of the procedure of the image processing method
The noise parameter setting step 3 is a type determining step, an intensity determining step, and a process determining step. In this noise parameter setting step, based on the fractal dimension Dim obtained in the analysis step of step s2, the type of noise to be superimposed on the input image for converting the input image into a predetermined image and the maximum value of the noise intensity, Also, the necessity of the superimposition processing is determined.

When the input image is a black-and-white binary image, there is a possibility that even if noise is superimposed, it hardly changes visually or deteriorates. Therefore, in the following description of the image processing method, An analysis image, which is a black and white gray image, is handled.

The procedure of the noise parameter setting process shown in FIG. 7 starts at step b0 and proceeds to step b1.
In step b0, first threshold value a and second threshold value
Set the threshold value b. The first threshold value a is 2.0 or more and less than the second threshold value b. The second threshold value b is greater than the first threshold value a and less than 3.0. As described above, the first threshold value a and the second threshold value b have the relationship of the following expression (6). 2.0 ≦ a <b <3.0 (6)

In step b1, the fractal dimension Dim obtained in the analysis step is 2.0 or more and the first threshold value a.
If it is determined that the fractal dimension Dim is 2.0 or more and less than the first threshold value a,
When it is determined that the fractal dimension Dim is not less than 2.0 and less than the first threshold value a, the process proceeds to step b2.

In step b1, the fractal dimension Di
When it is determined that m is 2.0 or more and less than the first threshold value a and the process proceeds to step b2, in step b2, the type of noise and the maximum value of noise intensity to be superimposed on the input image based on the fractal dimension Dim. The first noise parameter including the parameter is set, the process proceeds to step b3, the procedure of the noise parameter setting process is ended, and the process returns to the main routine of the image processing method shown in FIG.

In step b1, the fractal dimension Di
When it is determined that m is not 2.0 or more and less than the first threshold value a and the process proceeds to step b4, at step b4, the obtained fractal dimension Dim is not less than the first threshold value a and less than the second threshold value b. If it is determined that the fractal dimension Dim is greater than or equal to the first threshold value a and less than the second threshold value b, the process proceeds to step b5, and the fractal dimension Dim is greater than or equal to the first threshold value a. If it is determined that it is not less than the second threshold value b, the process proceeds to step b6.

In step b4, the fractal dimension Di
When it is determined that m is greater than or equal to the first threshold value a and less than the second threshold value b, and the process proceeds to step b5, in step b5, the type of noise and the strength of noise that are superimposed on the input image based on the fractal dimension Dim. The second noise parameter including the maximum value is set, the process proceeds to step b3, the procedure of the noise parameter setting process is ended, and the process returns to the main routine of the image processing method shown in FIG.

In step b4, the fractal dimension Di
If m is not greater than or equal to the first threshold value a and less than the second threshold value b, in other words, it is determined that the fractal dimension Dim is greater than or equal to the second threshold value and less than 3.0 and the process proceeds to step b6,
In step b6, the third noise parameter including the type of noise and the maximum value of noise intensity to be superimposed on the input image based on the fractal dimension Dim is set, and step b
3 and finish the procedure of the noise parameter setting process,
The procedure returns to the main routine of the image processing method shown in FIG.

FIG. 8 is a graph showing the relationship between the fractal dimension and the noise intensity in the first setting example of the setting of the first to third noise parameters. Table 1 is a table showing a first setting example of the settings of the first to third noise parameters.

[0063]

[Table 1]

In the first setting example of the setting of the first to third noise parameters shown in FIG. 8 and Table 1, the noise intensity is standardized so that the maximum absolute value of the noise intensity is 1. ing. This noise intensity is multiplied by the maximum value of the noise intensity shown in Table 1 to calculate the fractal dimension Dim.
Based on, the strength of noise superimposed on the input image is determined.

The fractal dimension Dim of the input image is 2.0.
When it is less than the first threshold value a, it is considered that the complexity of the input image is small, and when noise is superimposed on such an input image, the superimposed noise becomes visually conspicuous. Is determined as blue noise that is hard to be visually perceived, and the maximum value of noise intensity is determined as a small value of 1 or more and 3 or less.

When the fractal dimension Dim of the input image is greater than or equal to the first threshold value a and less than the second threshold value b, the complexity of the input image is considered to be large to some extent. / F noise or blue noise, and the maximum value of noise intensity is 5 or more 7
Determined below. The intensity of noise is gradually increased according to the fractal dimension.

When an output image obtained by subjecting an input image to image processing is formed on a recording paper, for example, an image is formed on plain paper by an ink jet type image forming apparatus or an electrophotographic image forming apparatus. If so, 1 / f noise is superimposed on the input image. The blue noise has a strong high-frequency spatial frequency component, and the image changes due to the superposition of the blue noise becomes severe. For this reason, when forming an input image on which blue noise is superimposed on a recording sheet, in order to improve noise reproducibility, an image forming apparatus having good dot reproducibility must be used. Therefore, when an image forming apparatus having low dot reproducibility is used, 1 / f noise is superimposed on the input image. Depending on the image forming apparatus used when forming an image, either blue noise or 1 / f noise may be selected in advance, and the selected type of noise may be superimposed on the input image.

When the fractal dimension Dim of the input image is not less than the second threshold value b and less than 3.0, it is considered that the complexity of the input image is very large, and noise is superimposed on such an input image. Also, since the noise is not noticeable visually,
Avoid superimposing noise. That is, there is no superimposed noise, and the maximum value of noise intensity is 0.

Further, the necessity degree of the superimposing process for superimposing noise on the input image is determined based on the fractal dimension Dim.
For example, in the first setting example shown in Table 1, it is determined that the necessity level increases as the maximum value of the noise intensity increases.

FIG. 9 is a graph showing the relationship between fractal dimension and noise intensity in the second setting example of the setting of the first to third noise parameters. Table 2 is a table showing a second setting example of the settings of the first to third noise parameters.

[0071]

[Table 2]

In the second setting example of the setting of the first to third noise parameters shown in FIG. 9 and Table 2, the noise intensity is standardized so that the maximum absolute value of the noise intensity is 1. ing. This noise intensity is multiplied by the maximum value of the noise intensity shown in Table 2 to calculate the fractal dimension Dim.
Based on, the strength of noise superimposed on the input image is determined. As shown in FIG. 9 and Table 2, by determining the type and intensity of noise and superimposing the noise on the input image, it is possible to effectively give the image a texture.

The fractal dimension Dim of the input image is 2.0.
When it is less than the first threshold value a, it is considered that the complexity of the input image is small. When noise is superimposed on such an input image, the superimposed noise becomes visually noticeable, and the noise is superimposed. Since the texture of the input image can be expected to be improved by this, the type of noise to be superimposed is determined to be 1 / f noise, and the maximum value of the noise intensity is determined to be a larger value of 5 or more and 7 or less.

When the fractal dimension Dim of the input image is greater than or equal to the first threshold value a and less than the second threshold value b, the complexity of the input image is considered to be large to some extent. Noise is determined, and the maximum value of noise intensity is determined to be a small value of 1 or more and 3 or less.

When the fractal dimension Dim of the input image is not less than the second threshold value b and less than 3.0, it is considered that the complexity of the input image is very large, and noise is superimposed on such an input image. Also, since the noise is not noticeable visually,
The type of noise to be superimposed is determined to be blue noise, and the maximum value of the noise intensity is determined to be 1 or more and 3 or less, or to prevent noise from being superimposed. The determination as to whether the superposition of blue noise or the absence of noise may be performed in advance, for example.

The determination of the noise intensity using the previously set FIG. 8 and Table 1, and FIG. 9 and Table 2 as described above is performed after each noise parameter is set. As shown in FIGS. 8 and 9, by normalizing the noise intensity with respect to the fractal dimension Dim so that the maximum absolute value of the noise intensity is 1, the noise intensity of Tables 1 and 2 is obtained. The noise intensity can be easily determined only by multiplying the maximum value of ω by the calculation formula shown in the graphs of FIGS. 8 and 9.

FIG. 10 is a flow chart showing a subroutine of the noise generating step of step s4 in the main routine of the procedure of the image processing method of FIG. The noise generation step of step s4 in the main routine of the procedure of the image processing method generates noise of the type and intensity determined in the noise parameter setting step of step s3. The procedure of the noise generation process shown in FIG. 10 starts at step c0 and proceeds to step c1.

FIG. 11 is a diagram showing a noise matrix, and FIG. 12 is a diagram showing how the frequency band is divided.
The noise matrix is used when noise is superimposed on the input image in the noise superimposing step of step s5 in the main routine of the image processing method described later. The noise matrix has H columns and K rows, and in the present embodiment, H = K = 64 elements (h, k).
And each element (h, k) has a noise value Noise.
(H, k) is set. h has a value of 0 or more (H-1) or less, and k has a value of 0 or more (K-1) or less.
The number H of columns and the number K of rows of this noise matrix may be set in advance by the user.

In step c1, the spatial frequency bands are equally divided into a plurality of bands so as not to overlap each other, and the power spectrum values of the respective divided bands are set so that the power spectrum is inversely proportional to the spatial frequency. Step c2
Proceed to. The spatial frequency band divided in step c1 depends on the resolution of the output image obtained by superimposing noise on the input image. For example, assuming that the output image has a horizontal and vertical resolution of 600 dpi, the spatial frequency range of this output image is -11.82 [lines / mm] or more and 11.82 in both the horizontal and vertical directions.
It is below [lines / mm].

From the property of Fourier transform, the range of the band for setting the power spectrum may be half the spatial frequency band of the image forming apparatus, that is, the Nyquist frequency. In the present embodiment, the frequency band to be divided is expressed by the following equation (7). −μ _N ≦ μ ≦ μ _N , 0 ≦ ν ≦ ν _N (7)

In the above equation (7), μ represents a horizontal spatial frequency, μ _N represents a horizontal Nyquist frequency, ν represents a vertical spatial frequency, and ν _N represents a vertical direction. Represents the Nyquist frequency of. The frequency band represented by the above equation (7) is equally divided into H × K / 2 pieces, and the power spectrum in each divided band is set.

As shown in Table 3, the power spectrum F (m, n) in the divided band (m, n) is a noise generation parameter preset for the type of noise superimposed on the input image. Using α and β, it is set as expressed by the following equation (8).

[0083]

[Table 3]

[0084]

[Equation 5]

In the above equation (8), m is the horizontal coordinate of the divided band, n is the vertical coordinate of the divided band, and Δμ is the horizontal coordinate of each divided band. Represents the length in the direction, and Δν represents the length in the vertical direction of each divided band.

At step c2, the phase φ (m, n) in the band (m, n) divided at step c1 is set as expressed by the following equation (9), and the process proceeds to step c3.

[0087]

[Equation 6]

In the above equation (9), RAND represents a random number, and 2πRAND represents a random number in the range of 0 or more and 2π or less. In the above equation (9), φ (m, n) is set to 0 when m = 0 and n = 0 so that the density value of the entire image data does not change by superimposing the noise of the DC component. This is because

At step c3, the noise value Noise in each element (h, k) forming the noise matrix
(H, k) is calculated based on the following equation (10) to generate a noise matrix, and the process proceeds to step c4.

[0090]

[Equation 7]

In the above equation (10), W (m, n) is
It is a weighting coefficient of each band (m, n) and is expressed by the following equation (11).

[0092]

[Equation 8]

In step c4, the noise value Noise (h, k) in each element (h, k) in the noise matrix calculated in step c3 is normalized to a range of -1 or more and 1 or less, and the process proceeds to step c5. In step c5, the noise value Noise in each element (h, k) in the noise matrix normalized in step c4
(H, k) is multiplied by an arbitrary value to amplify noise, and the process proceeds to step c6 to end the procedure of the noise generation process and return to the main routine of the image processing method shown in FIG. In step c4, the noise value Noise (h,
By normalizing k), the noise amplification in step c5 can be easily performed only by multiplying the normalized noise value by an arbitrary value.

In the noise superimposing step of step s5 in the main routine of the image processing method, the noise value corresponding to the pixel to be processed in the input image is read from the noise matrix shown in FIG. The value Noise (h, k) is added to superimpose noise on the input image, and the process proceeds to step s6 to end all the steps of the image processing method.

As described above, according to the image processing method of this embodiment, the fractal dimension of the input image is obtained in the analysis step, and the noise superimposed on the input image is calculated in the noise parameter setting step based on the fractal dimension. Determine type and noise intensity. Since the fractal dimension quantitatively represents the complexity of the input image, the noise that adapts to the complexity of the input image can be determined without trial and error to determine the type of noise and noise intensity to be superimposed on the input image. The type and the strength of noise can be smoothly determined. By superimposing the type of noise thus determined on the input image with the determined strength, the input image can be converted into a visually preferable image.

Further, according to the image processing method of the present embodiment, first, based on the fractal dimension, the types of noise prepared in advance as shown in Tables 1 and 2 are selected to suit the complexity of the input image. Corresponding the noise type to the noise intensity by making a stepwise decision and then applying the noise intensity to a calculation formula showing a graph as shown in FIGS. 8 and 9 to make a stepless determination. The amount of calculation can be significantly reduced compared to the case where the noise is determined steplessly, and the number of types of noise that are difficult to express as a calculation formula is suppressed to the smallest possible number, and the noise intensity is changed steplessly. By changing the type, it is possible to determine the type and intensity of noise such that a predetermined visual effect such as giving a texture to the input image can be obtained.

In the image processing method of this embodiment, step s in the flowchart of the image processing method of FIG.
After the analysis step of 2, a process determining step of determining the necessity of the superimposing process for superimposing noise on the input image based on the fractal dimension, and determining whether or not to perform the superimposing process based on the necessity. It may be included.

Accordingly, when the necessity of the superimposing process is high, in other words, when it is preferable to superpose noise on the input image, the superimposing process is performed, and when the necessity of the superimposing process is low, in other words, the input image is input. If it is preferable not to superpose noise on the image, the superimposing process is not performed, so that the input image can be surely converted into a visually preferable image.

If the process determining step is after the analyzing step,
Although the order of the other steps is not limited, the process determining step may be performed before the noise parameter setting step in order to reduce the number of steps up to the noise superimposing step. Further, in such a process determination step, the necessity degree of the superimposing process may be notified to the user, and the user may be prompted to input whether or not to perform the superimposing process. Further, for example, if the necessity degree is a fractal dimension and the fractal dimension is equal to or larger than the second threshold value b, that is, if the necessity degree is equal to or larger than the second threshold value b, the superimposition processing is not performed. A threshold value may be set and whether or not to perform the superimposing process may be determined depending on whether or not the necessity degree exceeds the threshold value.

In the image processing method of the present embodiment, the types of noise and the maximum value of noise intensity in each noise parameter shown in Tables 1 and 2 can be arbitrarily set by the user. May be. Further, the relationship between the fractal dimension and the noise intensity shown in FIGS. 8 and 9 may be arbitrarily set by the user.

The above-described image processing method is described in a predetermined programming language and causes the computer to execute this program. This allows each step of the image processing method to be automatically performed. By recording this program in a computer-readable recording medium,
It is possible to cause the computer to read the recording medium and execute the program recorded in the recording medium. Further, the program can be easily supplied to a plurality of computers via the recording medium.

The recording medium is, for example, a mask ROM or EP.
It is realized by a memory card having a semiconductor memory capable of recording and erasing data such as ROM, EEPROM and flash ROM, a magnetic disk such as a flexible disk, an optical disk such as CD-ROM and DVD, and a magneto-optical disk such as MO. .

The program may be supplied to the computer not only via a recording medium but also via a computer network to which the computer is connected. In this case, the program supplied to the computer via the computer network may be stored in a storage device such as a hard disk drive provided in the computer.

The input image when the computer executes the image processing method may be provided to the computer from a scanner device or a digital camera connectable to the computer, or may be provided to the computer via a computer network. You may An input image on which noise is superimposed is displayed by a display unit including a display device having a cathode ray tube connected to a computer and a liquid crystal display device, and is connected to a computer by an inkjet or electrophotographic printer. An image may be formed on the recording paper by an apparatus or the like.

The display means may display the necessity degree of the superimposing process or the maximum value of the noise type and the noise intensity determined in the noise parameter setting step. Alternatively, the maximum value of the noise type and the noise intensity may be input from an input means such as a keyboard and a mouse provided in the computer.

FIG. 13 is a block diagram showing the arrangement of an image forming apparatus 50 including the image processing apparatus 10 according to the embodiment of the present invention. The image forming apparatus 50 is the image processing apparatus 1.
0, an operation panel 21, an image input device 30, and an image output device 40. The image forming apparatus 50 superimposes, on the input image input from the image input apparatus 30, noise of the type and intensity determined by the image processing apparatus 10 based on the fractal dimension of the input image, and the image output apparatus 40 outputs the superimposed noise. An input image on which noise is superimposed is formed on a recording paper.

The image input device 30 irradiates a document on which an image is formed with light, for example, and displaces a scanner head having a plurality of photoelectric conversion elements (abbreviation: CCD) arranged in the main scanning direction in the sub scanning direction. The scanner device that converts the reflected light from the document into RGB analog reflectance signals and outputs the converted signals is also realized. The image output device 40 is realized by, for example, an inkjet printer device that forms an image by adhering ink to recording paper.

The image processing apparatus 10 includes an A / D converter 11,
Shading correction unit 12, input tone correction unit 13, fractal dimension analysis unit 14, color correction unit 15, image area separation processing unit 16, black generation undercolor processing unit 17, spatial filter processing unit 1.
8, noise superposition processing unit 19, halftone output gradation processing unit 20
And an operation panel 21.

The A / D converter 11 converts the analog reflectance signal of the input image from the image input device 30 into a digital reflectance signal and supplies it to the shading correction unit 12. The shading correction unit 12 performs shading correction processing on the reflectance signal from the A / D conversion unit 11,
It is applied to the input gradation correction unit 13. The shading correction process removes various distortions generated in the reflectance signal of the input image due to the configurations of the illumination system, the imaging system, and the imaging system of the image input device 30. The input gradation correction unit 13 performs input gradation correction processing on the reflectance signal from the shading correction unit 12 and outputs the result. The input tone correction process is a process of converting the reflectance signal into a signal suitable for image processing, such as an RGB density signal indicating the density of each color of RGB. In the input tone correction processing, color balance processing may be further performed on the reflectance signal.

FIG. 14 is a block diagram showing the configuration of the fractal dimension analyzing unit 14. Analysis means, type determination means,
The fractal dimension analysis unit 14, which is the intensity determination unit and the determination unit, is configured to include a brightness value conversion unit 141 and an analysis unit 142. The fractal dimension analysis unit 14 gives the RGB density signals from the input tone correction unit 13 to the color correction unit 15 described later. In addition, the fractal dimension analysis unit 14 obtains the fractal dimension of the input image from the RGB density signals from the input tone correction unit 13, and converts the input image into a visually preferable image based on the fractal dimension. The type of noise to be superimposed on is determined, and the intensity of noise indicating the degree of conversion by the noise to the input image is determined based on the fractal dimension. Further, the fractal dimension analysis unit 14 determines the necessity degree of the superimposing process for superimposing noise on the input image based on the fractal dimension,
An analysis signal including the fractal dimension of the input image, the type of noise to be superimposed on the input image, the strength of the type of noise to be superimposed on the input image, and the necessity of the superimposing process is sent to the noise superimposing processing unit 19 and the operation panel 21 described later. give.

The brightness value conversion unit 141 is the input gradation correction unit 1.
The density value of each color shown in the RGB density signals from 3 is set to 1
The input image is converted into a monochrome grayscale image by converting it into one luminance value, and a monochrome input image signal indicating the input image that has become the monochrome grayscale image is output. In the brightness value conversion unit 141, the monochrome grayscale image conversion process of step s1 in the flowchart of the image processing method shown in FIG. 1 is performed. The analysis unit 142 obtains a fractal dimension from the black-and-white input image signal from the luminance value conversion unit 141, and based on the fractal dimension, converts the input image into a visually preferable image. In addition to determining the type, noise intensity is determined based on the fractal dimension. Further, the analysis unit 142 determines the necessity degree of the superimposing process for superimposing noise on the input image based on the fractal dimension. Further, the analysis unit 42 uses the fractal dimension of the input image,
An analysis signal including the type of noise to be superimposed on the input image, the strength of the type of noise to be superimposed on the input image, and the necessity of the superimposing process is given to the noise superimposing processing unit 19 and the operation panel 21. In the analysis unit 142, the analysis step of step s2 in the flowchart of the image processing method shown in FIG.
And the noise parameter setting step of step s3 is performed, and in detail, it is performed according to the flowcharts shown in FIG. 2 and FIG.

The color correction unit 15 includes the fractal dimension analysis unit 1.
4 is converted into CNY density signals indicating the densities of cyan, magenta, and yellow, and color correction processing is performed on the CMY density signals in order to faithfully reproduce colors in the image output device 40. Then, the CMY density signal is given to the black generation undercolor processing unit 17 and the image area separation processing unit 16. Specifically, the color correction process is
Color turbidity based on the spectral characteristics of cyan, magenta, and yellow (CMY) inks and toners that respectively contain unnecessary absorption components is removed from the CMY density signal.

The image area separation processing section 16 applies the CMY density signal from the color correction section 15 to a character area, a halftone area, and an area excluding the character and the halftone dot for each pixel or for each block composed of a plurality of pixels. Region separation processing for separation is performed, and the region identification signal that is the separation result is used as the black generation and undercolor processing unit 1.
7, the spatial filter processing unit 18 and the halftone output gradation processing unit 20.

The black generation and undercolor processing unit 17 determines, based on the CMY color signals included in the CMY density signals from the color correction unit 15,
In addition to performing black generation processing for generating a black color signal, C
Undercolor removal processing is performed on the MY color signals. The undercolor removal processing is processing for subtracting the black color signal generated in the black generation processing from the CMY color signal to obtain a new CMY color signal. As described above, the black generation and undercolor processing unit 17 includes the CMY density signal from the color correction unit 15 including the CMY color signal obtained by subtracting the black color signal and the black color signal.
It is converted to a K color signal and given to the spatial filter processing unit 18.

The spatial filter processing section 18 performs spatial filter processing on the CMYK color signal of the input image from the black generation and undercolor processing section 17 using a digital filter, and noises the CMYK color signal subjected to the spatial filter processing. Superposition processing unit 1
Give to 9. Since the spatial frequency characteristic of the image is corrected by this, when the image output device 40 forms an image on the recording paper, it is possible to prevent the formed image from being blurred and from having graininess deterioration. The spatial filter processing may be performed based on the area identification signal from the image area separation processing unit 16.

FIG. 15 is a block diagram showing the structure of the noise superposition processing section 19. The noise superimposition processing unit 19, which is a superimposition processing unit, includes a control unit 191, a storage unit 192, a noise generation unit 193, and a noise superposition unit 194. The noise superposition processing unit 19 is a fractal dimension analysis unit 1.
The input image based on the type of noise to be superimposed on the input image and the intensity of the type of noise to be superimposed on the input image included in the analysis signal from 4 and the processing information included in the control signal from the operation panel 21. Superimposition processing is performed on the CMYK color signals from the spatial filter processing unit 18 so that noise is superposed on.

The control unit 191 includes the fractal dimension analysis unit 1
Whether or not the noise generation unit 193 is caused to generate noise based on the fractal dimension of the input image included in the analytic signal from 4 and the necessity of the superimposition processing, and the processing information included in the control signal from the operation panel 21 described later. It gives a generation control signal.

The storage unit 192 stores the relationship between the fractal dimension and the noise intensity. Specifically, the graphs shown in FIGS. 8 and 9 in the above-described image processing method, or a look-up table (look up ta) corresponding to this graph.
ble; abbreviated name: LUT).

The noise generation unit 193 stores the type of noise and the maximum value of noise intensity included in the analytic signal supplied from the fractal dimension analysis unit 14 via the control unit 191 (see Tables 1 and 2), and stores it. When noise is generated by generating blue noise, white noise, and 1 / f noise based on the generation control signal from the control unit 191, based on the relationship between the fractal dimension stored in the unit 192 and the strength of noise. Gives a superimposed noise signal indicating noise to the noise superimposing unit 194.

The noise superimposing unit 194 adds noise to the CMYK color signals of the input image from the spatial filter processing unit 18 from the noise generating unit 193, thereby superimposing noise on the input image to reduce noise. The CMYK color signals of the superimposed input image are output to the halftone output gradation processing unit 2
Give to 0.

The halftone output gradation processing unit 20 outputs the CMY of the input image on which the noise from the noise superimposing processing unit 19 is superimposed.
The K color signal is subjected to gradation correction processing and halftone generation processing, and is given to the image output device 40. The halftone generation process is a process of dividing an input image on which noise is superimposed into a plurality of pixels so that gradations can be reproduced, and uses a binary or multivalued dither method, an error diffusion method, or the like. You can Further, the halftone output gradation processing unit 20 is a CM of the input image.
The density value included in the YK color signal may be converted into a halftone dot area ratio, which is a characteristic value of the image output device 40.

The operation panel 21 which is the display means and the input means is realized by a display device such as a liquid crystal display device and an input device such as a touch panel. Operation panel 2
1 indicates the fractal dimension of the input image included in the analysis signal from the fractal dimension analysis unit 14, the type of noise to be superimposed on the input image, the strength of noise to be superimposed on the input image, and the necessity degree of the superimposing process. , A sentence is displayed that asks the user whether or not noise should be superimposed on the input image. The content displayed on the operation panel 21 may be content that allows the user to select the type and strength of noise, or content that informs the user whether or not noise should be superimposed. Further, when the operation panel 21 is input by a user and processing information indicating whether or not to perform a superimposing process for superimposing noise on an input image is input,
The control signal including the processing information is given to the noise superimposition processing unit 19.

FIG. 16 is a flowchart showing the procedure of image processing in the image forming apparatus 50. Step t0
Then, the image processing procedure is started, and the process proceeds to step t1.
At step t1, a message "Do you wish to perform noise superimposition processing? Yes / No" is displayed on the operation panel 21. The operation panel 21 is input by the user,
When the mode selection as to whether or not to perform the superimposing processing is performed, the operation panel 21 gives a control signal including processing information as to whether or not to perform the superimposing processing to the noise superimposing processing unit 19, and step t2.
Proceed to. When the user selects the mode in which the superimposing process is not performed, the procedure regarding the superimposing process is not performed.

At step t1, a description of what the superimposing process is, and a sample image before the superimposing process and a sample image after the superimposing process are given to the operation panel 21.
May be displayed on the screen.

At step t2, in order to determine whether or not the input image is a complicated image, a prescan is performed by the image input device 30 before the formal input image is input by the image input device 30, and step t3 Proceed to.

At step t3, the fractal dimension analysis of the input image prescanned at step t2 is performed, and the process proceeds to step t4. The fractal dimension analysis is performed by the fractal dimension analysis unit 14 in the same manner as the analysis step of step s2 and the noise parameter setting step of step s3 in the flowchart of the image processing method shown in FIG.

At step t4, the fractal dimension of the input image obtained at step t3, the type of noise superimposed on the input image, the strength of noise superimposed on the input image,
And the necessity degree of the superimposing process is displayed on the operation panel 21, and a sentence such as a question asking the user whether to superpose noise on the input image is displayed on the operation panel 21, and the process proceeds to step t5. By displaying the fractal dimension of the input image, the type of noise to be superimposed on the input image, the strength of the noise to be superimposed on the input image, and the necessity of the superimposing process, for example, when the input image is very complicated,
In other words, when the fractal dimension is large and close to 3.0, even if noise is superimposed, the change may not be visually recognized. Therefore, by displaying it on the operation panel 21, the user can see it. It is possible to notify the user and entrust it to the user to decide whether or not to perform the superimposing process.

At step t5, the user determines whether or not noise should be superimposed on the input image, and inputs the processing information as to whether or not to perform the superimposing processing by inputting the operation on the operation panel 21. The control signal including the processing information from the user is given to the noise superimposition processing unit 19. The noise superimposition processing unit 19
Based on the processing information, it is determined whether or not the superimposing process is performed. When it is determined that the superimposing process is performed, the process proceeds to step t6,
When it is determined that the superimposing process is not performed, the image output device 40 forms an input image on which noise is not superimposed on the recording paper, proceeds to step t7, and ends all the procedures.

When it is determined in step t5 that the superimposing process is to be performed, the process proceeds to step t6.
The noise superimposition processing unit 20 superimposes noise on the input image, and the image output device 40 forms an image of the output image on which noise is superposed on the input image on recording paper, and proceeds to step t7 to end all procedures. To do.

As described above, according to the image processing apparatus 10 of the present embodiment, the fractal dimension analysis unit 14 obtains the fractal dimension of the input image, and based on the fractal dimension, the input image is a visually preferable image. The type of noise to be superimposed on the input image and the maximum value of the intensity of the noise for conversion into the noise are determined, and the noise superimposition processing unit 19 superimposes the type of noise determined by the fractal dimension analysis unit 14 on the input image. . Since the fractal dimension quantitatively represents the complexity of the input image, the type of noise that adapts to the complexity of the input image and the type of noise that adapts to the complexity of the input image can be obtained without trial and error to determine the type and strength of the noise to be superimposed on the input image. The strength can be determined automatically and smoothly. By superimposing the noise of the type and strength thus determined on the input image, the input image can be converted into a visually preferable image.

Further, the fractal dimension analysis unit 14 determines the necessity degree of the superimposing processing for superimposing noise on the input image based on the fractal dimension, and the necessity degree of the superimposing processing is displayed on the operation panel 21. Further, the processing information indicating whether or not to execute the superimposition processing is input by the user's input operation from the operation panel 21, and the noise superimposition processing unit 19 causes the operation panel 21
When a control signal including processing information for executing the superimposing process is given from, the superimposing process is performed. As a result, the user can select whether the superimposition processing displayed on the operation panel 21 is necessary.
The processing information can be input to the operation panel 21 by selecting whether or not to perform the superimposition processing.

According to the image forming apparatus 50 having the image processing apparatus 10 of the present embodiment, it is possible to realize the image forming apparatus capable of achieving the above-described actions and effects.
As a result, the input image can be converted into a visually preferable image and the image can be formed on the recording paper.

The image forming apparatus 50 of this embodiment uses a flatbed scanner, a film scanner and a digital camera as the image input apparatus 30, and uses the image output apparatus 4
An ink jet type printer device and an electrophotographic printer device are used as 0, and a display device having a cathode ray tube and a liquid crystal display device instead of the operation panel 21 which is a display device and an input device; It may be realized by a computer system to which these devices are connected by using an input unit realized by an input device such as a keyboard and a mouse. Such a computer system may include communication means for connecting to another computer such as a server via a computer network.

[0134]

As described above, according to the present invention, the type of noise adapted to the complexity of the input image can be smoothly determined without trial and error for determining the type of noise to be superimposed on the input image. be able to. By superimposing the type of noise thus determined on the input image, the input image can be converted into a visually preferable image.

Further, according to the present invention, it is possible to determine the strength of noise superimposed on the input image without trial and error.
It is possible to smoothly determine the noise intensity that adapts to the complexity of the input image. By superimposing noise on the input image with the strength thus determined, the input image can be converted into a visually preferable image.

Further, according to the present invention, when the necessity of the superimposing processing is high, in other words, when it is preferable to superpose noise on the input image, the superimposing processing is performed, and when the necessity of the superimposing processing is low, In other words, when it is preferable not to superimpose noise on the input image, the superimposing process is not performed,
The input image can be reliably converted into a visually pleasing image.

Further, according to the present invention, the above effects can be automatically realized by the computer.

Further, according to the present invention, it is possible to cause the computer to read and execute the recorded program to execute the above-described image processing method. Further, the program can be easily supplied to a plurality of computers via the recording medium.

Further, according to the present invention, it is possible to determine the kind of noise to be superimposed on the input image without trial and error.
The type of noise adapted to the complexity of the input image can be automatically and smoothly determined. By superimposing the type of noise thus determined on the input image, the input image can be converted into a visually preferable image.

Further, according to the present invention, it is possible to determine the strength of the noise superimposed on the input image without trial and error.
It is possible to automatically and smoothly determine the strength of noise that adapts to the complexity of the input image. By superimposing noise on the input image with the strength thus determined, the input image can be converted into a visually preferable image.

Further, according to the present invention, the user can select whether or not to perform the superimposing processing according to the necessity of the superimposing processing displayed on the display means, and input the processing information to the input means. it can.

Further, according to the present invention, it is possible to realize an image forming apparatus capable of achieving the above effects. This transforms the input image into a visually pleasing image,
The image can be formed on the recording paper.

[Brief description of drawings]

FIG. 1 is a flowchart showing a main routine of a procedure of an image processing method according to an embodiment of the present invention.

FIG. 2 is a flow chart showing a subroutine of an analysis process of step s2 in the main routine of the procedure of the image processing method of FIG.

FIG. 3 is a diagram showing an example of an analysis image G1 which is a binary image.

FIG. 4 is a diagram showing a grid-divided analysis image G1.

FIG. 5 is a graph showing a relationship between log r and log N (r) and a regression line.

FIG. 6 is a diagram showing an image space S including an analysis image G2.

7 is a flowchart showing a subroutine of a noise parameter setting step of step s3 in the main routine of the procedure of the image processing method of FIG.

FIG. 8 is a graph showing a relationship between fractal dimension and noise intensity in a first setting example of setting first to third noise parameters.

FIG. 9 is a graph showing a relationship between fractal dimension and noise intensity in a second setting example of setting first to third noise parameters.

10 is a flowchart showing a subroutine of a noise generation process of step s4 in the main routine of the procedure of the image processing method of FIG.

FIG. 11 is a diagram showing a noise matrix.

FIG. 12 is a diagram showing how the frequency band is divided.

FIG. 13 is a block diagram showing a configuration of an image forming apparatus 50 including the image processing apparatus 10 according to the embodiment of the present invention.

FIG. 14 is a block diagram showing a configuration of a fractal dimension analysis unit 14.

FIG. 15 is a block diagram showing a configuration of a noise superimposition processing unit 19.

16 is a flowchart showing a procedure of image processing in the image forming apparatus 50. FIG.

[Explanation of symbols]

10 Image processing device 14 Fractal dimension analysis unit 19 Noise superposition processing unit 21 Operation panel 50 image forming apparatus

Claims (9)

[Claims] 1. An image processing method, comprising: an analyzing step of obtaining a fractal dimension of an input image; and a type determining step of determining a type of noise to be superimposed on the input image based on the obtained fractal dimension. 2. The image according to claim 1, further comprising, after the type determining step, an intensity determining step of determining a noise intensity indicating a degree of conversion of the input image due to noise, based on the fractal dimension. Processing method. 3. After the analysis step, a process of determining the necessity of a superimposing process for superimposing noise on an input image based on the fractal dimension, and deciding whether or not to perform the superimposing process based on the necessity. The image processing method according to claim 1, further comprising a determining step. 4. A program for causing a computer to execute the image processing method according to claim 1. 5. A computer-readable recording medium in which the program according to claim 4 is recorded. 6. An analyzing means for obtaining a fractal dimension of an input image, a type determining means for determining a type of noise to be superimposed on an input image based on the fractal dimension obtained by the analyzing means, and a type determining means. An image processing apparatus, comprising: a superimposing processing unit that superimposes different types of noise on an input image. 7. The strength determination means for determining the strength of noise indicating the degree of conversion by the noise to the input image based on the fractal dimension, and the superposition processing means includes the strength of noise determined by the strength determination means. 7. The image processing apparatus according to claim 6, wherein noise is superimposed on the input image by. 8. A determination means for determining the necessity of a superimposing process for superimposing noise on an input image based on the fractal dimension, the necessity of the superimposing process determined by the determining means is displayed on the display means, and the superimposing is performed. 8. The superimposition processing means performs the superimposition processing when the processing information for executing the superimposition processing is input by the input means for inputting the processing information as to whether or not to execute the processing. Image processing device. 9. An image forming apparatus comprising the image processing apparatus according to claim 6.