Liu, C.J.[Chang-Jiang],
Cheng, I.[Irene],
Zhang, Y.[Yi],
Basu, A.[Anup],
Enhancement of low visibility aerial images using histogram
truncation and an explicit Retinex representation for balancing
contrast and color consistency,
PandRS(128), No. 1, 2017, pp. 16-26.
Elsevier DOI
1706
Multi-scale, Retinex
BibRef
Li, M.D.[Ma-Ding],
Liu, J.Y.[Jia-Ying],
Yang, W.H.[Wen-Han],
Sun, X.Y.[Xiao-Yan],
Guo, Z.M.[Zong-Ming],
Structure-Revealing Low-Light Image Enhancement Via Robust Retinex
Model,
IP(27), No. 6, June 2018, pp. 2828-2841.
IEEE DOI
1804
image enhancement, optimisation,
augmented Lagrange multiplier based alternating
direction minimization algorithm, structure-revealing.
BibRef
Ren, X.,
Yang, W.,
Cheng, W.,
Liu, J.,
LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex
Model,
IP(29), 2020, pp. 5862-5876.
IEEE DOI
2005
Lighting, Robustness, Noise reduction, Histograms, Minimization,
Visualization, Estimation, Low-light enhancement, denoising,
low-rank decomposition
BibRef
Gu, Z.,
Li, F.,
Fang, F.,
Zhang, G.,
A Novel Retinex-Based Fractional-Order Variational Model for Images
With Severely Low Light,
IP(29), 2020, pp. 3239-3253.
IEEE DOI
2002
Retinex, low-light image, fractional-order, variational model,
image enhancement, reflectance, illumination
BibRef
Yang, W.,
Wang, W.,
Huang, H.,
Wang, S.,
Liu, J.,
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light
Image Enhancement,
IP(30), 2021, pp. 2072-2086.
IEEE DOI
2101
Lighting, Image restoration, Image enhancement, Image coding,
Noise reduction, Atmospheric modeling, Minimization,
denoising
BibRef
Li, M.[Miao],
Zhou, D.M.[Dong-Ming],
Nie, R.C.[Ren-Can],
Xie, S.D.[Shi-Dong],
Liu, Y.Y.[Yan-Yu],
AMBCR: Low-Light Image Enhancement via Attention Guided Multi-Branch
Construction and Retinex Theory,
IET-IPR(15), No. 9, 2021, pp. 2020-2038.
DOI Link
2106
attention networks, low-light image enhancement,
multi-branch construction, Retinex theory
BibRef
Yang, J.Y.[Jing-Yu],
Xu, Y.W.[Yu-Wei],
Yue, H.J.[Huan-Jing],
Jiang, Z.Y.[Zhong-Yu],
Li, K.[Kun],
Low-light image enhancement based on Retinex decomposition and
adaptive gamma correction,
IET-IPR(15), No. 5, 2021, pp. 1189-1202.
DOI Link
2106
BibRef
Kong, X.Y.[Xiang-Yu],
Liu, L.[Lei],
Qian, Y.S.[Yun-Sheng],
Low-Light Image Enhancement via Poisson Noise Aware Retinex Model,
SPLetters(28), 2021, pp. 1540-1544.
IEEE DOI
2108
Lighting, Reflectivity, Kernel, Image enhancement, Noise reduction,
Photonics, Standards, Image denoising, low-light image enhancement,
Retinex model
BibRef
Lv, X.Q.[Xiao-Qian],
Sun, Y.J.[Yu-Jing],
Zhang, J.[Jun],
Jiang, F.[Feng],
Zhang, S.P.[Sheng-Ping],
Low-light image enhancement via deep Retinex decomposition and
bilateral learning,
SP:IC(99), 2021, pp. 116466.
Elsevier DOI
2111
Low-light image enhancement, Image decomposition,
Deep neural network, Attention, Illumination adjustment
BibRef
Zhao, Z.J.[Zun-Jin],
Xiong, B.S.[Bang-Shu],
Wang, L.[Lei],
Ou, Q.F.[Qiao-Feng],
Yu, L.[Lei],
Kuang, F.[Fa],
RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement,
CirSysVideo(32), No. 3, March 2022, pp. 1076-1088.
IEEE DOI
2203
Lighting, Couplings, Image enhancement, Task analysis, Histograms,
Cameras, Low-light image enhancement, zero-reference
BibRef
Lin, Y.H.[Yi-Hsien],
Lu, Y.C.[Yi-Chang],
Low-Light Enhancement Using a Plug-and-Play Retinex Model With
Shrinkage Mapping for Illumination Estimation,
IP(31), 2022, pp. 4897-4908.
IEEE DOI
2208
Lighting, Optimization, Image edge detection, Stars,
Learning systems, Linear programming, Image quality,
alternating direction method of multipliers
BibRef
Rasheed, M.T.[Muhammad Tahir],
Guo, G.Y.[Gui-Yu],
Shi, D.M.[Da-Ming],
Khan, H.[Hufsa],
Cheng, X.C.[Xiao-Chun],
An Empirical Study on Retinex Methods for Low-Light Image Enhancement,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link
2209
BibRef
Zhao, Z.J.[Zun-Jin],
Shi, D.M.[Da-Ming],
Retinex-guided generative diffusion prior for low-light image
enhancement,
PR(172), 2026, pp. 112421.
Elsevier DOI Code:
WWW Link.
2512
Low-light enhancement, Retinex, Diffusion, Training-free enhancement
BibRef
Zhao, Z.J.[Zun-Jin],
Lin, H.X.[He-Xiu],
Shi, D.M.[Da-Ming],
Zhou, G.Q.[Guo-Qing],
A non-regularization self-supervised Retinex approach to low-light
image enhancement with parameterized illumination estimation,
PR(146), 2024, pp. 110025.
Elsevier DOI Code:
WWW Link.
2311
Low-light image enhancement, Illumination estimation,
Parameterization, Bilateral grid, Non-regularization
BibRef
Wang, Y.[Yong],
Li, B.[Bo],
Jiang, L.J.[Li-Jun],
Yang, W.M.[Wen-Ming],
R2Net: Relight the restored low-light image based on complementarity
of illumination and reflection,
SP:IC(108), 2022, pp. 116800.
Elsevier DOI
2209
Low-light image enhancement, Retinex-based method,
Attention module, Image restoration
BibRef
Jia, F.[Fan],
Wong, H.S.[Hok Shing],
Wang, T.[Tiange],
Zeng, T.Y.[Tie-Yong],
A reflectance re-weighted Retinex model for non-uniform and low-light
image enhancement,
PR(144), 2023, pp. 109823.
Elsevier DOI
2310
Image enhancement, Variational method, Retinex model, Non-uniform enhancement
BibRef
Jia, F.[Fan],
Mao, S.[Shen],
Tai, X.C.[Xue-Cheng],
Zeng, T.Y.[Tie-Yong],
A Variational Model for Nonuniform Low-Light Image Enhancement,
SIIMS(17), No. 1, 2024, pp. 1-30.
DOI Link
2404
BibRef
Ma, Q.T.[Qian-Ting],
Wang, Y.[Yang],
Zeng, T.Y.[Tie-Yong],
A Contrast-Saturation Adaptive Model for Low-Light Image Enhancement,
SIIMS(18), No. 1, 2025, pp. 765-787.
DOI Link
2504
BibRef
Hai, J.[Jiang],
Hao, Y.T.[Yu-Tong],
Zou, F.Z.[Feng-Zhu],
Lin, F.[Fang],
Han, S.C.[Song-Chen],
Advanced RetinexNet: A fully convolutional network for low-light
image enhancement,
SP:IC(112), 2023, pp. 116916.
Elsevier DOI
2302
Retinex, Low-light image enhancement, Image processing,
Fully convolutional network
BibRef
Yang, J.[Jie],
Wang, J.[Jun],
Dong, L.L.[Lin-Lu],
Chen, S.Y.[Shu-Yuan],
Wu, H.[Hao],
Zhong, Y.W.[Ya-Wen],
Optimization algorithm for low-light image enhancement based on
Retinex theory,
IET-IPR(17), No. 2, 2023, pp. 505-517.
DOI Link
2302
fast and robust fuzzy C-means, guided filtering,
image enhancement, low-light image
BibRef
Wang, Y.N.[Yong-Nian],
Zhang, Z.B.[Zhi-Bin],
Global attention retinex network for low light image enhancement,
JVCIR(92), 2023, pp. 103795.
Elsevier DOI
2303
Low light image enhancement, Retinex, Global attention, Channel attention
BibRef
Lu, H.X.[Hao-Xiang],
Liu, Z.B.[Zhen-Bing],
Lan, R.[Rushi],
Pan, X.P.[Xi-Peng],
Gong, J.M.[Jun-Ming],
Retinex-inspired contrast stretch and detail boosting for lowlight
image enhancement,
IET-IPR(17), No. 6, 2023, pp. 1718-1738.
DOI Link
2305
image enhancement, image fusion, image processing
BibRef
Yang, B.[Biao],
Zheng, L.L.[Liang-Liang],
Wu, X.B.[Xia-Bin],
Gao, T.[Tan],
Chen, X.L.[Xiao-Long],
Low-Illumination Image Enhancement Using Local Gradient Relative
Deviation for Retinex Models,
RS(15), No. 17, 2023, pp. 4327.
DOI Link
2310
BibRef
Ma, L.[Long],
Liu, R.S.[Ri-Sheng],
Wang, Y.Y.[Yi-Yang],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
Low-Light Image Enhancement via Self-Reinforced Retinex Projection
Model,
MultMed(25), 2023, pp. 3573-3586.
IEEE DOI
2310
BibRef
An, N.[Nan],
Ma, L.[Long],
Han, G.C.[Guang-Chao],
Fan, X.[Xin],
Liu, R.S.[Ri-Sheng],
Striving for Faster and Better: A One-Layer Architecture With Auto
Re-Parameterization for Low-Light Image Enhancement,
CirSysVideo(35), No. 8, August 2025, pp. 7455-7470.
IEEE DOI Code:
WWW Link.
2508
Visualization, Image enhancement, Computational efficiency,
Lighting, Reflectivity, Computational modeling, Optimization,
tiered architecture search
BibRef
Ma, L.[Long],
Jin, D.[Dian],
An, N.[Nan],
Liu, J.Y.[Jin-Yuan],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
Liu, R.S.[Ri-Sheng],
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement,
IJCV(133), No. 11, November 2025, pp. 8234-8252.
Springer DOI
2511
BibRef
Ma, L.[Long],
Ma, T.Y.[Teng-Yu],
Liu, R.S.[Ri-Sheng],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
Toward Fast, Flexible, and Robust Low-Light Image Enhancement,
CVPR22(5627-5636)
IEEE DOI
2210
Training, Adaptation models, Visualization, Image segmentation,
Computational modeling, Semantics, Lighting, Low-level vision
BibRef
Ma, L.[Long],
Ma, T.Y.[Teng-Yu],
Xu, C.P.[Cheng-Pei],
Liu, J.Y.[Jin-Yuan],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
Liu, R.S.[Ri-Sheng],
Learning with Self-Calibrator for Fast and Robust Low-Light Image
Enhancement,
PAMI(47), No. 10, October 2025, pp. 9095-9112.
IEEE DOI
2510
Lighting, Image enhancement, Training, Visualization, Estimation,
Convergence, Architecture, Reflectivity, Optimization, low-light vision
BibRef
Ma, Q.T.[Qian-Ting],
Wang, Y.[Yang],
Zeng, T.Y.[Tie-Yong],
Retinex-Based Variational Framework for Low-Light Image Enhancement
and Denoising,
MultMed(25), 2023, pp. 5580-5588.
IEEE DOI
2311
BibRef
Zhou, M.L.[Ming-Liang],
Wu, X.T.[Xing-Tai],
Wei, X.K.[Xue-Kai],
Xiang, T.[Tao],
Fang, B.[Bin],
Kwong, S.[Sam],
Low-Light Enhancement Method Based on a Retinex Model for Structure
Preservation,
MultMed(26), 2024, pp. 650-662.
IEEE DOI
2402
Lighting, Reflectivity, Image enhancement, Dispersion, Standards, Sensitivity,
Histograms, Coefficient of variation, retinex model, structure-preserving
BibRef
Li, X.F.[Xiao-Fang],
Wang, W.W.[Wei-Wei],
Feng, X.C.[Xiang-Chu],
Li, M.[Min],
Deep Parametric Retinex Decomposition Model for Low-Light Image
Enhancement,
CVIU(241), 2024, pp. 103948.
Elsevier DOI
2403
Low-light image enhancement, Parametric Retinex decomposition,
Deep network, Denoising
BibRef
Wang, Z.X.[Ze-Xin],
Qingge, L.[Letu],
Pan, Q.Y.[Qing-Yi],
Yang, P.[Pei],
Retinex decomposition based low-light image enhancement by
integrating Swin transformer and U-Net-like architecture,
IET-IPR(18), No. 11, 2024, pp. 3028-3041.
DOI Link
2409
low-light image enhancement, residual connection,
swin transformer, U-Net
BibRef
Weng, R.[Ruidi],
Zhang, Y.[Ya],
Wu, H.Y.[Han-Yang],
Wang, W.Y.[Wei-Yong],
Wang, D.Y.[Dong-Yun],
The Retinex enhancement algorithm for low-light intensity image based
on improved illumination map,
IET-IPR(18), No. 12, 2024, pp. 3381-3392.
DOI Link
2411
image denoising, image enhancement, image processing
BibRef
Chen, L.W.[Li-Wei],
Liu, Y.Y.[Yan-Yan],
Li, G.[Guoning],
Hong, J.T.[Jin-Tao],
Li, J.[Jin],
Peng, J.T.[Jian-Tao],
Double-function enhancement algorithm for low-illumination images
based on retinex theory,
JOSA-A(40), No. 2, February 2023, pp. 316-325.
DOI Link
2503
Deep learning, Image metrics, Image quality, Machine vision,
Neural networks, Wavelet transforms
BibRef
Chen, X.Y.[Xin-Yu],
Li, J.J.[Jin-Jiang],
Hua, Z.[Zhen],
Low-Light Image Enhancement Based on Exponential Retinex Variational
Model,
IET-IPR(15), No. 12, 2021, pp. 3003-3019.
DOI Link
2109
BibRef
Xu, X.T.[Xin-Tao],
Li, J.J.[Jin-Jiang],
Hua, Z.[Zhen],
Fan, L.W.[Lin-Wei],
Attention-Based Multi-Channel Feature Fusion Enhancement Network to
Process Low-Light Images,
IET-IPR(16), No. 12, 2022, pp. 3374-3393.
DOI Link
2209
BibRef
Yu, N.[Nana],
Li, J.J.[Jin-Jiang],
Hua, Z.[Zhen],
LBP-Based Progressive Feature Aggregation Network for Low-Light Image
Enhancement,
IET-IPR(16), No. 2, 2022, pp. 535-553.
DOI Link
2201
BibRef
Feng, X.M.[Xiao-Mei],
Li, J.J.[Jin-Jiang],
Fan, H.[Hui],
Hierarchical guided network for low-light image enhancement,
IET-IPR(15), No. 13, 2021, pp. 3254-3266.
DOI Link
2110
BibRef
Li, J.J.[Jin-Jiang],
Feng, X.M.[Xiao-Mei],
Hua, Z.[Zhen],
Low-Light Image Enhancement via Progressive-Recursive Network,
CirSysVideo(31), No. 11, November 2021, pp. 4227-4240.
IEEE DOI
2112
Image enhancement, Lighting, Training, Feature extraction,
Brightness, Task analysis, Image color analysis,
attention model
BibRef
Huang, Z.X.[Zhi-Xiong],
Li, J.J.[Jin-Jiang],
Hua, Z.[Zhen],
Fan, L.W.[Lin-Wei],
Attention-Based Dual-Color Space Fusion Network for Low-Light Image
Enhancement,
SP:IC(119), 2023, pp. 117060.
Elsevier DOI
2310
Low-light image enhancement, Dual-color space,
Adaptive large-kernel attention, Deep learning
See also Underwater Image Enhancement via LBP-Based Attention Residual Network.
BibRef
Cai, R.T.[Rong-Tai],
Chen, Z.K.[Ze-Kun],
Brain-like retinex: A biologically plausible retinex algorithm for
low light image enhancement,
PR(136), 2023, pp. 109195.
Elsevier DOI
2301
Retinex, Low light image enhancement, Contour detection,
Edge detection, Brain-inspired computation, Color constancy, Retinal circuit
BibRef
Gao, X.Y.[Xing-Yun],
Zhang, W.B.[Wei-Bo],
Zhuang, P.X.[Pei-Xian],
Zhao, W.[Wenyi],
Zhang, W.D.[Wei-Dong],
RDANet: Retinex decomposition attention network for low-light image
enhancement,
PRL(197), 2025, pp. 175-181.
Elsevier DOI
2510
Low-light image enhancement, Cascaded attention, Retinex decomposition
BibRef
Qiang, H.[Hu],
Zhong, Y.Z.[Yu-Zhong],
Liao, Y.W.[Yi-Wei],
You, X.X.[Xing-Xing],
Zhu, Y.Q.[Yu-Qi],
Dian, S.[Songyi],
GWRetinex-Net: Gray World Retinex Network for Low-Light Image
Enhancement,
CirSysVideo(35), No. 10, October 2025, pp. 9636-9649.
IEEE DOI
2510
Lighting, Reflection, Image color analysis, Image enhancement,
Distortion, Degradation, Deep learning, Brightness, Training, Noise,
color balance
BibRef
Li, W.C.[Wen-Chao],
Wen, S.Y.[Shu-Yuan],
Zhu, J.H.[Jin-Hao],
Ou, Q.[Qiaofeng],
Guo, Y.C.[Yan-Chun],
Chen, J.[Jiabao],
Xiong, B.S.[Bang-Shu],
ZERRIN-Net: Adaptive low-light image enhancement using Retinex
decomposition and noise extraction,
SP:IC(138), 2025, pp. 117345.
Elsevier DOI Code:
WWW Link.
2509
Low-light image enhancement, Image denoise, Zero-shot learning,
Retinex decomposition
BibRef
Yi, X.P.[Xun-Peng],
Xu, H.[Han],
Zhang, H.[Hao],
Tang, L.F.[Lin-Feng],
Ma, J.Y.[Jia-Yi],
Diff-Retinex++: Retinex-Driven Reinforced Diffusion Model for
Low-Light Image Enhancement,
PAMI(47), No. 8, August 2025, pp. 6823-6841.
IEEE DOI
2507
BibRef
Earlier:
Diff-Retinex: Rethinking Low-light Image Enhancement with A
Generative Diffusion Model,
ICCV23(12268-12277)
IEEE DOI
2401
Diffusion models, Image enhancement, Image restoration,
Degradation, Lighting, Image synthesis, Noise reduction, mixture of experts
BibRef
Zhang, K.B.[Kai-Bing],
Yuan, C.[Cheng],
Li, J.[Jie],
Gao, X.B.[Xin-Bo],
Li, M.Q.[Min-Qi],
Multi-Branch and Progressive Network for Low-Light Image Enhancement,
IP(32), 2023, pp. 2295-2308.
IEEE DOI
2305
Image enhancement, Lighting, Degradation, Image color analysis,
Network architecture, Technological innovation, Task analysis,
recurrent network
BibRef
Wang, H.K.[Hua-Ke],
Hou, X.S.[Xing-Song],
Li, J.T.[Ju-Tao],
Yan, Y.[Yadi],
Sun, W.K.[Wen-Ke],
Zeng, X.[Xin],
Zhang, K.B.[Kai-Bing],
Cao, X.Y.[Xiang-Yong],
Multi-Scale Retinex Unfolding Network for Low-Light Image Enhancement,
MultMed(27), 2025, pp. 5709-5721.
IEEE DOI
2509
Image enhancement, Lighting, Computational modeling, Optimization,
Transformers, Image restoration, Image color analysis, Data mining,
scale-aware proximal mapping
BibRef
Wu, K.[Kangle],
Huang, J.[Jun],
Ma, Y.[Yong],
Fan, F.[Fan],
Ma, J.Y.[Jia-Yi],
Cycle-Retinex: Unpaired Low-Light Image Enhancement via
Retinex-Inline CycleGAN,
MultMed(26), 2024, pp. 1213-1228.
IEEE DOI
2402
Image enhancement, Image color analysis, Image restoration, Lighting,
Colored noise, Degradation, Deep learning, unsupervised learning
BibRef
Xu, H.[Han],
Zhang, H.[Hao],
Yi, X.P.[Xun-Peng],
Ma, J.Y.[Jia-Yi],
CRetinex: A Progressive Color-Shift Aware Retinex Model for Low-Light
Image Enhancement,
IJCV(132), No. 1, January 2024, pp. 3610-3632.
Springer DOI
2409
BibRef
Kang, S.C.[Si-Cong],
Gao, S.B.[Shuai-Bo],
Wu, W.H.[Wen-Hui],
Wang, X.[Xu],
Wang, S.[Shuoyao],
Qiu, G.P.[Guo-Ping],
Image Intrinsic Components Guided Conditional Diffusion Model for
Low-Light Image Enhancement,
CirSysVideo(34), No. 12, December 2024, pp. 13244-13256.
IEEE DOI Code:
WWW Link.
2501
Lighting, Image restoration, Reflectivity, Diffusion models,
Feature extraction, Image color analysis, Image enhancement,
retinex decomposition
See also Deep Feature Prior-Guided Conditional Diffusion Model for Underwater Image Enhancement.
BibRef
Liu, C.X.[Chun-Xiao],
Wang, Z.[Zelong],
Birch, P.[Philip],
Wang, X.[Xun],
Efficient Retinex-Based Framework for Low-Light Image Enhancement
Without Additional Networks,
CirSysVideo(35), No. 5, May 2025, pp. 4896-4909.
IEEE DOI
2505
Lighting, Degradation, Image enhancement, Image color analysis,
Estimation, Transforms, Colored noise, multiscale attention network
BibRef
Guo, R.[Ruoyu],
Pagnucco, M.[Maurice],
Song, Y.[Yang],
Exploring Multi-Feature Relationship in Retinex Decomposition for
Low-Light Image Enhancement,
MultMed(27), 2025, pp. 7271-7284.
IEEE DOI
2510
Reflectivity, Lighting, Image color analysis, Feature extraction,
Image enhancement, Training, Brightness, Unsupervised learning,
Retinex theory
BibRef
Ma, A.[Ailin],
Jiang, H.[Hai],
Liang, B.B.[Bin-Bin],
Han, S.C.[Song-Chen],
Incorporating Fourier Transformation With Diffusion Models for
Low-Light Image Enhancement,
SPLetters(32), 2025, pp. 2354-2358.
IEEE DOI
2507
Image restoration, Image reconstruction, Diffusion models,
Noise reduction, Training, Degradation, Image enhancement,
low-light image enhancement
BibRef
Jiang, H.[Hai],
Luo, A.[Ao],
Liu, X.H.[Xiao-Hong],
Han, S.C.[Song-Chen],
Liu, S.C.[Shuai-Cheng],
Lightendiffusion: Unsupervised Low-light Image Enhancement with
Latent-retinex Diffusion Models,
ECCV24(XLVIII: 161-179).
Springer DOI
2412
BibRef
Sun, S.Q.[Shang-Quan],
Ren, W.Q.[Wen-Qi],
Peng, J.Y.[Jing-Yang],
Song, F.L.[Feng-Long],
Cao, X.C.[Xiao-Chun],
DI-Retinex: Digital-Imaging Retinex Model for Low-Light Image
Enhancement,
IJCV(133), No. 12, December 2025, pp. 8293-8314.
Springer DOI
2512
See also Restoring Images in Adverse Weather Conditions via Histogram Transformer.
BibRef
Torres, D.[Daniel],
Duran, J.[Joan],
Navarro, J.[Julia],
Sbert, C.[Catalina],
Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin
for Low-Light Image Enhancement,
IJCV(133), No. 11, November 2025, pp. 7772-7793.
Springer DOI
2511
BibRef
And: A1, A4, A2, Only:
A Retinex-Based Variational Model with A Nonlocal Gradient-Type
Constraint for Low-Light Image Enhancement,
ICIP25(1187-1192)
IEEE DOI
2601
Reflectivity, Deep learning, Accuracy, Computational modeling, Noise,
Noise reduction, Brightness, Lighting, Image decomposition,
nonlocal method
BibRef
Hsieh, P.W.[Po-Wen],
Yang, S.Y.[Suh-Yuh],
Anisotropic pth-order TV-based Retinex decomposition with adaptive
reflectance regularizer for low-light image enhancement,
PR(172), 2026, pp. 112468.
Elsevier DOI
2512
Low-light image enhancement, Retinex decomposition,
Total variation, Noise suppression
BibRef
Xiao, Y.[Yao],
Xia, Y.S.[You-Shen],
A novel image enhancement method based on image decomposition and
deep neural networks,
PR(172), 2026, pp. 112371.
Elsevier DOI Code:
WWW Link.
2512
Low-light image enhancement, Image decomposition,
Deep neural network, Retinex theory
BibRef
Hu, E.[Enping],
Liu, Y.[Yun],
Wang, A.Z.[An-Zhi],
Shiri, B.[Babak],
Ren, W.Q.[Wen-Qi],
Lin, W.S.[Wei-Si],
Low-Light Image Enhancement Using a Retinex-Based Variational Model
with Weighted L_p Norm Constraint,
MultMed(28), 2026, pp. 152-166.
IEEE DOI
2601
Lighting, Reflectivity, Noise, Image enhancement, Training,
Computational modeling, Image restoration, Image edge detection,
Retinex decomposition
See also Single Image Dehazing Based on Weighted Variational Regularized Model.
BibRef
Zhang, J.[Jin],
Jin, H.Y.[Hai-Yan],
Su, H.N.[Hao-Nan],
Zhang, Y.L.[Yuan-Lin],
Xiao, Z.L.[Zhao-Lin],
Wang, B.[Bin],
A CNN-Transformer Network Based SNR Guided High Frequency
Reconstruction for Low Light Image Enhancement,
ICIP24(1649-1655)
IEEE DOI
2411
Visualization, Image edge detection, Noise reduction, Transformers,
Image decomposition, Image restoration, High frequency, SNR
BibRef
Cai, Y.H.[Yuan-Hao],
Bian, H.[Hao],
Lin, J.[Jing],
Wang, H.Q.[Hao-Qian],
Timofte, R.[Radu],
Zhang, Y.L.[Yu-Lun],
Retinexformer: One-stage Retinex-based Transformer for Low-light
Image Enhancement,
ICCV23(12470-12479)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lecert, A.[Arthur],
Fraisse, R.[Renaud],
Roumy, A.[Aline],
Guillemot, C.[Christine],
A New Regularization for Retinex Decomposition of Low-Light Images,
ICIP22(906-910)
IEEE DOI
2211
Measurement, Lighting, Image restoration, Image enhancement,
Light sources, Low light enhancement, Image decomposition, Neural Networks
BibRef
Wu, Q.[Qi],
Qin, M.L.[Mao-Ling],
Song, J.Q.[Jing-Qi],
Liu, L.[Li],
An Improved Method of Low Light Image Enhancement Based on Retinex,
ICIVC21(233-241)
IEEE DOI
2112
Lighting, Reflection, Image decomposition, Feeds, Task analysis,
Image enhancement, low light, image enhancement, Retinex
BibRef
Liu, R.S.[Ri-Sheng],
Ma, L.[Long],
Zhang, J.[Jiaao],
Fan, X.[Xin],
Luo, Z.X.[Zhong-Xuan],
Retinex-inspired Unrolling with Cooperative Prior Architecture Search
for Low-light Image Enhancement,
CVPR21(10556-10565)
IEEE DOI
2111
Deep learning, Architecture, Computational modeling, Lighting,
Search problems
BibRef
Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Color Constancy, Recognition, Simon Fraser Univ. (Funt and Finlayson) Papers .