Panda et al., 2024 - Google Patents
Integrating graph convolution into a deep multilayer framework for low-light image enhancementPanda et al., 2024
- Document ID
- 6795812006988073389
- Author
- Panda S
- Sa P
- Publication year
- Publication venue
- IEEE Sensors Letters
External Links
Snippet
Digital camera sensors may struggle to capture images in low-light environments, resulting in lower brightness and contrast levels, color degradation, undesirable characteristics, and noise. Such reduced-quality images adversely affect the performance of computer vision …
- 238000000034 method 0 abstract description 29
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cui et al. | Semi-supervised image deraining using knowledge distillation | |
| Ma et al. | FusionGAN: A generative adversarial network for infrared and visible image fusion | |
| Kim et al. | Fully deep blind image quality predictor | |
| Panda et al. | Integrating graph convolution into a deep multilayer framework for low-light image enhancement | |
| Chobola et al. | Fast context-based low-light image enhancement via neural implicit representations | |
| Raza et al. | IR-MSDNet: Infrared and visible image fusion based on infrared features and multiscale dense network | |
| Guo et al. | MDFN: Mask deep fusion network for visible and infrared image fusion without reference ground-truth | |
| Wang et al. | WaveFusion: A novel wavelet vision transformer with saliency-guided enhancement for multimodal image fusion | |
| Wu et al. | Mini-infrared thermal imaging system image denoising with multi-head feature fusion and detail enhancement network | |
| Liu et al. | Multi-focus color image fusion algorithm based on super-resolution reconstruction and focused area detection | |
| Yang et al. | LatLRR-CNN: An infrared and visible image fusion method combining latent low-rank representation and CNN | |
| Liu et al. | Dual UNet low-light image enhancement network based on attention mechanism | |
| Singh et al. | A review on computational low-light image enhancement models: Challenges, benchmarks, and perspectives | |
| Xiao et al. | Spatial invertible network with mamba-convolution for hyperspectral image fusion | |
| Li et al. | Application of convolutional neural networks for parallel multi-scale feature extraction in noise image denoising | |
| Sun et al. | HAIAFusion: a hybrid attention illumination-aware framework for infrared and visible image fusion | |
| Zhang et al. | DMRO-Fusion: Infrared and visible image fusion based on recurrent-octave auto-encoder via two-level modulation | |
| Liu et al. | CM-MCNet: Convolution and multilayer perceptron-integrated multiscale coordinate network for infrared and visible image fusion | |
| Tang et al. | Infrared and visible image fusion based on guided hybrid model and generative adversarial network | |
| Wang et al. | BDPartNet: Feature decoupling and reconstruction fusion network for infrared and visible image | |
| Wang et al. | Lgabl: Uhd multi-exposure image fusion via local and global aware bilateral learning | |
| Weligampola et al. | A retinex based gan pipeline to utilize paired and unpaired datasets for enhancing low light images | |
| Zhou et al. | DFVO: Learning darkness-free visible and infrared image disentanglement and fusion all at once | |
| Singh et al. | Multiscale reflection component based weakly illuminated nighttime image enhancement | |
| Zhang et al. | Multi-scale feature enhancement and fusion network for real-world infrared image denoising |