Xu et al., 2022 - Google Patents

Single infrared image stripe removal via deep multi-scale dense connection convolutional neural network

Xu et al., 2022

Document ID
10960997527648260914
Author
Xu K
Zhao Y
Li F
Xiang W
Publication year
Publication venue
Infrared Physics & Technology

External Links

Snippet

Stripe noise removal is a crucial step for the infrared imaging system. Existing stripe removal methods are hard to balance stripe removal and image details preservation. In this paper, a deep multi-scale dense connection convolutional neural network (DMD-CNN) is proposed to …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • G06T2207/20064Wavelet transform [DWT]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/002Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20016Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20172Image enhancement details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007Dynamic range modification
    • G06T5/008Local, e.g. shadow enhancement
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • G06T3/4084Transform-based scaling, e.g. FFT domain scaling
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations

Similar Documents

Publication Publication Date Title
Xu et al. Single infrared image stripe removal via deep multi-scale dense connection convolutional neural network
Wang et al. Hyperreconnet: Joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging
He et al. Fast hyperspectral image recovery of dual-camera compressive hyperspectral imaging via non-iterative subspace-based fusion
Iliadis et al. Deep fully-connected networks for video compressive sensing
Cai et al. Binarized spectral compressive imaging
Li et al. Hyperspectral pansharpening via improved PCA approach and optimal weighted fusion strategy
CN112819910A (en) Hyperspectral image reconstruction method based on double-ghost attention machine mechanism network
Dharejo et al. A deep hybrid neural network for single image dehazing via wavelet transform
Xu et al. Pan-denoising: Guided hyperspectral image denoising via weighted represent coefficient total variation
CN116739899A (en) Image super-resolution reconstruction method based on SAUGAN network
Chen et al. Image denoising via deep network based on edge enhancement
Wang et al. A generative image fusion approach based on supervised deep convolution network driven by weighted gradient flow
Liu et al. MMF: A Multi-scale MobileNet based fusion method for infrared and visible image
Xu et al. AACNet: Asymmetric attention convolution network for hyperspectral image dehazing
Pu et al. Removing rain based on a cycle generative adversarial network
Du et al. Low-light image enhancement and denoising via dual-constrained Retinex model
Zhou et al. RDFNet: regional dynamic FISTA-Net for spectral snapshot compressive imaging
Yu et al. Two-stage image decomposition and color regulator for low-light image enhancement
Shi et al. LCA-net: A context-aware lightweight network for low-illumination image enhancement
Arya et al. ADMM optimizer for integrating wavelet-patch and group-based sparse representation for image inpainting
Tang et al. Infrared and visible image fusion based on guided hybrid model and generative adversarial network
Zheng et al. Spectral super-resolution for high-accuracy rice variety classification using hybrid CNN-Transformer model
Lang et al. Spatial-spectral middle cross-attention fusion network for hyperspectral image superresolution
Tao et al. DWTN: deep wavelet transform network for lightweight single image deraining
Qu et al. Compressive hyperspectral imaging based on images structure similarity and deep image prior