5.6.3.1 Low Light Enhancement

Chapter Contents (Back)
Low Light. Enhancement.

Kang, B.H.[Bong-Hyup], Jeon, C.W.[Chang-Won], Han, D.K.[David K.], Ko, H.S.[Han-Seok],
Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation,
IVC(29), No. 8, July 2011, pp. 557-568.
Elsevier DOI 1108
Backlight compensation; Image enhancement; Contrast enhancement; Histogram equalization; Saturation BibRef

Park, D.[Dubok], Kim, M.J.[Min-Jae], Ku, B.[Bonhwa], Yoon, S.[Sangmin], Han, D.K.[David K.],
Image enhancement for extremely low light conditions,
AVSS14(307-312)
IEEE DOI 1411
Dynamic range BibRef

Jung, C.[Cheolkon], Yang, Q.[Qi], Sun, T.T.[Ting-Ting], Fu, Q.T.[Qing-Tao], Song, H.[Hyoseob],
Low light image enhancement with dual-tree complex wavelet transform,
JVCIR(42), No. 1, 2017, pp. 28-36.
Elsevier DOI 1701
Contrast enhancement BibRef

Su, H.[Haonan], Yu, L.[Long], Jung, C.[Cheolkon],
Joint Contrast Enhancement and Noise Reduction of Low Light Images Via JND Transform,
MultMed(24), 2022, pp. 17-32.
IEEE DOI 2202
Visualization, Noise reduction, Transforms, Image color analysis, Colored noise, Adaptation models, Lighting, Contrast enhancement, Weber's law BibRef

Lore, K.G.[Kin Gwn], Akintayo, A.[Adedotun], Sarkar, S.[Soumik],
LLNet: A deep autoencoder approach to natural low-light image enhancement,
PR(61), No. 1, 2017, pp. 650-662.
Elsevier DOI 1705
Image enhancement BibRef

Lim, J.[Jaemoon], Heo, M.[Minhyeok], Lee, C.[Chul], Kim, C.S.[Chang-Su],
Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition,
JVCIR(45), No. 1, 2017, pp. 107-121.
Elsevier DOI 1704
Image enhancement BibRef

Lim, J.[Jaemoon], Kim, J.H.[Jin-Hwan], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Robust contrast enhancement of noisy low-light images: Denoising-enhancement-completion,
ICIP15(4131-4135)
IEEE DOI 1512
Low-light image enhancement BibRef

Kim, J.H.[Jin-Hwan], Jang, W.D.[Won-Dong], Park, Y.[Yongsup], Lee, D.H.[Dong-Hahk], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Temporally x real-time video dehazing,
ICIP12(969-972).
IEEE DOI 1302
BibRef

Kim, J.H.[Jin-Hwan], Jang, W.D.[Won-Dong], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Optimized contrast enhancement for real-time image and video dehazing,
JVCIR(24), No. 3, April 2013, pp. 410-425.
Elsevier DOI 1303
Image dehazing; Video dehazing; Image restoration; Contrast enhancement; Temporal coherence; Image enhancement; Optimized dehazing; Atmospheric light estimation BibRef

Ko, S.Y.[Seung-Yong], Yu, S.[Soohwan], Park, S.[Seonhee], Moon, B.[Byeongho], Kang, W.[Wonseok], Paik, J.[Joonki],
Variational framework for low-light image enhancement using optimal transmission map and combined and -minimization,
SP:IC(58), No. 1, 2017, pp. 99-110.
Elsevier DOI 1710
Low-light, image, enhancement BibRef

Anaya, J.[Josue], Barbu, A.[Adrian],
RENOIR-A dataset for real low-light image noise reduction,
JVCIR(51), 2018, pp. 144-154.
Elsevier DOI 1802
Dataset, Noise Reduction. Image denoising, Denoising dataset, Low light noise, Poisson-Gaussian noise model BibRef

Guo, X., Li, Y., Ling, H.,
LIME: Low-Light Image Enhancement via Illumination Map Estimation,
IP(26), No. 2, February 2017, pp. 982-993.
IEEE DOI 1702
estimation theory BibRef

Li, C.Y.[Chong-Yi], Guo, J.C.[Ji-Chang], Porikli, F.M.[Fatih M.], Pang, Y.W.[Yan-Wei],
LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement,
PRL(104), 2018, pp. 15-22.
Elsevier DOI 1804
Low light image enhancement, Weak illumination image enhancement, Image degradation, CNNs BibRef

Jiang, X.S.[Xue-Song], Yao, H.X.[Hong-Xun], Liu, D.L.[Di-Lin],
Nighttime image enhancement based on image decomposition,
SIViP(13), No. 1, February 2019, pp. 189-197.
WWW Link. 1901
BibRef

Yu, S., Zhu, H.,
Low-Illumination Image Enhancement Algorithm Based on a Physical Lighting Model,
CirSysVideo(29), No. 1, January 2019, pp. 28-37.
IEEE DOI 1901
Lighting, Attenuation, Image restoration, Image color analysis, Degradation, Scattering, Atmospheric modeling, Image enhancement, weighted guide filter BibRef

Tang, C.Y.[Chao-Ying], Wang, Y.[Yeru], Feng, H.J.[Hua-Jun], Xu, Z.H.[Zhi-Hai], Li, Q.[Qi], Chen, Y.T.[Yue-Ting],
Low-light image enhancement with strong light weakening and bright halo suppressing,
IET-IPR(13), No. 3, February 2019, pp. 537-542.
DOI Link 1903
BibRef

Ren, Y.[Yurui], Ying, Z.Q.[Zhen-Qiang], Li, T.H., Li, G.[Ge],
LECARM: Low-Light Image Enhancement Using the Camera Response Model,
CirSysVideo(29), No. 4, April 2019, pp. 968-981.
IEEE DOI 1904
Cameras, Lighting, Image enhancement, Image color analysis, Nonlinear distortion, Histograms, Camera response function, contrast enhancement BibRef

Ying, Z.Q.[Zhen-Qiang], Li, G.[Ge], Ren, Y.[Yurui], Wang, R.G.[Rong-Gang], Wang, W.M.[Wen-Min],
A New Low-Light Image Enhancement Algorithm Using Camera Response Model,
CVPV17(3015-3022)
IEEE DOI 1802
BibRef
And:
A New Image Contrast Enhancement Algorithm Using Exposure Fusion Framework,
CAIP17(II: 36-46).
Springer DOI 1708
Cameras, Distortion, Estimation, Image color analysis, Image enhancement, Lighting, Mathematical model BibRef

Loh, Y.P.[Yuen Peng], Liang, X.F.[Xue-Feng], Chan, C.S.[Chee Seng],
Low-light image enhancement using Gaussian Process for features retrieval,
SP:IC(74), 2019, pp. 175-190.
Elsevier DOI 1904
Low-light, Image enhancement, Gaussian Process, Convolutional neural network BibRef

Ren, W.Q.[Wen-Qi], Liu, S.F.[Si-Fei], Ma, L.[Lin], Xu, Q.Q.[Qian-Qian], Xu, X.Y.[Xiang-Yu], Cao, X.C.[Xiao-Chun], Du, J.P.[Jun-Ping], Yang, M.H.[Ming-Hsuan],
Low-Light Image Enhancement via a Deep Hybrid Network,
IP(28), No. 9, Sep. 2019, pp. 4364-4375.
IEEE DOI 1908
image enhancement, recurrent neural nets, content stream, encoder-decoder network, edge stream, edge details, auto-encoder, recurrent neural network BibRef

Wang, Y.F.[Yun-Fei], Liu, H.M.[He-Ming], Fu, Z.W.[Zhao-Wang],
Low-Light Image Enhancement via the Absorption Light Scattering Model,
IP(28), No. 11, November 2019, pp. 5679-5690.
IEEE DOI 1909
Lighting, Atmospheric modeling, Imaging, Image color analysis, Mathematical model, Image enhancement, Absorption, minimal channel BibRef

Fu, G., Duan, L., Xiao, C.,
A Hybrid L2 -LP Variational Model For Single Low-Light Image Enhancement With Bright Channel Prior,
ICIP19(1925-1929)
IEEE DOI 1910
Retinex, reflectance, illumination, alternating minimization BibRef

Wu, Y.H.[Ya-Hong], Zheng, J.Y.[Jie-Ying], Song, W.[Wanru], Liu, F.[Feng],
Low light image enhancement based on non-uniform illumination prior model,
IET-IPR(13), No. 13, November 2019, pp. 2448-2456.
DOI Link 1911
BibRef

Wu, Y.H.[Ya-Hong], Song, W.[Wanru], Zheng, J.Y.[Jie-Ying], Liu, F.[Feng],
Non-uniform low-light image enhancement via non-local similarity decomposition model,
SP:IC(93), 2021, pp. 116141.
Elsevier DOI 2103
Non-uniform low-light image enhancement, Similarity measure, Non-local similarity decomposition, Edge information, Reflectance and illumination BibRef

Liu, X.L.[Xia-Lin], Sun, Y.[Yiwei], Shi, J.H.[Jian-Hong], Zeng, G.H.[Gui-Hua],
Photon efficiency of computational ghost imaging with single-photon detection,
JOSA-A(35), No. 10, October 2018, pp. 1741-1748.
DOI Link 1912
Computational imaging, Digital micromirror devices, Image quality, Imaging systems, Low light level techniques, Low light levels BibRef

Lee, H., Sohn, K., Min, D.,
Unsupervised Low-Light Image Enhancement Using Bright Channel Prior,
SPLetters(27), 2020, pp. 251-255.
IEEE DOI 2002
Unsupervised learning, low-light image enhancement, bright channel prior BibRef

Srinivas, K.[Kankanala], Bhandari, A.K.[Ashish Kumar],
Low light image enhancement with adaptive sigmoid transfer function,
IET-IPR(14), No. 4, 27 March 2020, pp. 668-678.
DOI Link 2003
BibRef

Hsieh, P.W.[Po-Wen], Shao, P.C.A.[Pei-Chi-Ang], Yang, S.Y.[Suh-Yuh],
Adaptive Variational Model for Contrast Enhancement of Low-Light Images,
SIIMS(13), No. 1, 2020, pp. 1-28.
DOI Link 2004
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

Yang, W.H.[Wen-Han], Yuan, Y.[Ye], Ren, W.Q.[Wen-Qi], Liu, J.Y.[Jia-Ying], Scheirer, W.J.[Walter J.], Wang, Z.Y.[Zhang-Yang], Zhang, T.H.[Tai-Heng], Zhong, Q.Y.[Qiao-Yong], Xie, D.[Di], Pu, S.L.[Shi-Liang], Zheng, Y.Q.[Yu-Qiang], Qu, Y.Y.[Yan-Yun], Xie, Y.H.[Yu-Hong], Chen, L.[Liang], Li, Z.H.[Zhong-Hao], Hong, C.[Chen], Jiang, H.[Hao], Yang, S.Y.[Si-Yuan], Liu, Y.[Yan], Qu, X.C.[Xiao-Chao], Wan, P.F.[Peng-Fei], Zheng, S.[Shuai], Zhong, M.H.[Min-Hui], Su, T.Y.[Tai-Yi], He, L.Z.[Ling-Zhi], Guo, Y.D.[Yan-Dong], Zhao, Y.[Yao], Zhu, Z.F.[Zhen-Feng], Liang, J.X.[Jin-Xiu], Wang, J.W.[Jing-Wen], Chen, T.Y.[Tian-Yi], Quan, Y.H.[Yu-Hui], Xu, Y.[Yong], Liu, B.[Bo], Liu, X.[Xin], Sun, Q.[Qi], Lin, T.Y.[Ting-Yu], Li, X.C.[Xiao-Chuan], Lu, F.[Feng], Gu, L.[Lin], Zhou, S.D.[Sheng-Di], Cao, C.[Cong], Zhang, S.F.[Shi-Feng], Chi, C.[Cheng], Zhuang, C.B.[Chu-Bing], Lei, Z.[Zhen], Li, S.Z.[Stan Z.], Wang, S.Z.[Shi-Zheng], Liu, R.Z.[Rui-Zhe], Yi, D.[Dong], Zuo, Z.M.[Zhe-Ming], Chi, J.N.[Jian-Ning], Wang, H.[Huan], Wang, K.[Kai], Liu, Y.X.[Yi-Xiu], Gao, X.Y.[Xing-Yu], Chen, Z.Y.[Zhen-Yu], Guo, C.[Chang], Li, Y.Z.[Yong-Zhou], Zhong, H.C.[Hui-Cai], Huang, J.[Jing], Guo, H.[Heng], Yang, J.F.[Jian-Fei], Liao, W.J.[Wen-Juan], Yang, J.G.[Jian-Gang], Zhou, L.G.[Li-Guo], Feng, M.Y.[Ming-Yue], Qin, L.K.[Li-Kun],
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study,
IP(29), 2020, pp. 5737-5752.
IEEE DOI 2005
Poor visibility environment, object detection, face detection, haze, rain, low-light conditions BibRef

Xiang, T., Yang, Y., Guo, S.,
Blind Night-Time Image Quality Assessment: Subjective and Objective Approaches,
MultMed(22), No. 5, May 2020, pp. 1259-1272.
IEEE DOI 2005
Measurement, Distortion, Feature extraction, Image quality, Visualization, Image databases, Blind image quality assessment, gray-level co-occurrence matrix BibRef

Saha, R.[Rappy], Banik, P.P.[Partha Pratim], Gupta, S.S.[Shantanu Sen], Kim, K.D.[Ki-Doo],
Combining highlight removal and low-light image enhancement technique for HDR-like image generation,
IET-IPR(14), No. 9, 20 July 2020, pp. 1851-1861.
DOI Link 2007
BibRef

Wang, L., Liu, Z., Siu, W., Lun, D.P.K.,
Lightening Network for Low-Light Image Enhancement,
IP(29), 2020, pp. 7984-7996.
IEEE DOI 2007
Feature extraction, Task analysis, Lighting, Image resolution, Reflectivity, Image enhancement, Data mining, deep learning BibRef

Kim, J.W.[Jae-Woo], Ryu, J.H.[Je-Ho], Kim, J.O.[Jong-Ok],
Deep gradual flash fusion for low-light enhancement,
JVCIR(72), 2020, pp. 102903.
Elsevier DOI 1806
Image fusion, Flash fusion, Pseudo multi-exposure, Auto-encoder, GAN, Low light enhancement BibRef

Xie, J.[Junyi], Bian, H.[Hao], Wu, Y.[Yuanhang], Zhao, Y.[Yu], Shan, L.[Linmin], Hao, S.J.[Shi-Jie],
Semantically-guided low-light image enhancement,
PRL(138), 2020, pp. 308-314.
Elsevier DOI 2010
Image enhancement, Low light, Semantic information, Simplified retinex model BibRef

Hao, S., Han, X., Guo, Y., Xu, X., Wang, M.,
Low-Light Image Enhancement With Semi-Decoupled Decomposition,
MultMed(22), No. 12, December 2020, pp. 3025-3038.
IEEE DOI 2011
Lighting, Task analysis, Imaging, Image edge detection, Image enhancement, Visualization, Optimization, Low-light images, Retinex model BibRef

Fu, Q.X.[Qing-Xu], Di, X.G.[Xiao-Guang], Zhang, Y.[Yu],
Learning an adaptive model for extreme low-light raw image processing,
IET-IPR(14), No. 14, December 2020, pp. 3433-3443.
DOI Link 2012
BibRef

Lamba, M., Rachavarapu, K.K., Mitra, K.,
Harnessing Multi-View Perspective of Light Fields for Low-Light Imaging,
IP(30), 2021, pp. 1501-1513.
IEEE DOI 2101
Image restoration, Cameras, Light fields, Noise reduction, Estimation, ISO, Visualization, Low-light, light field enhancement, light field dataset BibRef

Hu, L.S.[Lin-Shu], Qin, M.J.[Meng-Jiao], Zhang, F.[Feng], Du, Z.H.[Zhen-Hong], Liu, R.Y.[Ren-Yi],
RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
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

Zhu, Z., Meng, Y., Kong, D., Zhang, X., Guo, Y., Zhao, Y.,
To See in the Dark: N2DGAN for Background Modeling in Nighttime Scene,
CirSysVideo(31), No. 2, February 2021, pp. 492-502.
IEEE DOI 2102
Lighting, Training, Surveillance, Image enhancement, Generators, Integrated circuit modeling, GAN, background model, Bayes theory BibRef

Niu, J.X.[Jin-Xing], Jiang, Y.J.[Ya-Jie], Fu, Y.Y.[Ya-Yun],
Research on image sharpening algorithm in weak illumination environment,
IET-IPR(14), No. 15, 15 December 2020, pp. 3635-3638.
DOI Link 2103
BibRef

Yang, W.H.[Wen-Han], Wang, S.Q.[Shi-Qi], Fang, Y.M.[Yu-Ming], Wang, Y.[Yue], Liu, J.Y.[Jia-Ying],
Band Representation-Based Semi-Supervised Low-Light Image Enhancement: Bridging the Gap Between Signal Fidelity and Perceptual Quality,
IP(30), 2021, pp. 3461-3473.
IEEE DOI 2103
BibRef
Earlier:
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement,
CVPR20(3060-3069)
IEEE DOI 2008
Visualization, Lighting, Neural networks, Image enhancement, Image color analysis, Degradation, Visual perception, Low light, perceptual quality. Lighting, Estimation, Image restoration, Colored noise BibRef

Zhang, Y.H.[Yong-Hua], Guo, X.J.[Xiao-Jie], Ma, J.Y.[Jia-Yi], Liu, W.[Wei], Zhang, J.W.[Jia-Wan],
Beyond Brightening Low-light Images,
IJCV(129), No. 4, April 2021, pp. 1013-1037.
Springer DOI 2104
BibRef

Liu, J.Y.[Jia-Ying], Xu, D.J.[De-Jia], Yang, W.H.[Wen-Han], Fan, M.H.[Min-Hao], Huang, H.F.[Hao-Feng],
Benchmarking Low-Light Image Enhancement and Beyond,
IJCV(129), No. 4, April 2021, pp. 1153-1184.
Springer DOI 2104
BibRef

Huang, H.F.[Hao-Feng], Yang, W.H.[Wen-Han], Hu, Y.Y.[Yue-Yu], Liu, J.Y.[Jia-Ying],
Raw-guided Enhancing Reprocess of Low-light Image via Deep Exposure Adjustment,
ACCV20(II:118-133).
Springer DOI 2103
BibRef

Wang, J.S.[Jun-Shu], Yang, Y.[Yue], Chen, Y.[Yuan], Han, Y.X.[Yu-Xing],
LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Lv, F.F.[Fei-Fan], Li, Y.[Yu], Lu, F.[Feng],
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset,
IJCV(129), No. 7, July 2021, pp. 2175-2193.
Springer DOI 2106
BibRef

Miller, S.[Sarah], Zhang, C.[Chen], Hirakawa, K.[Keigo],
Multi-Resolution Aitchison Geometry Image Denoising for Low-Light Photography,
IP(30), 2021, pp. 5724-5738.
IEEE DOI 2106
Noise reduction, Wavelet transforms, Lighting, Geometry, Estimation, Image denoising, Image edge detection, Image denoising, Poisson, Aitchison geometry 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

Sandoub, G.[Ghada], Atta, R.[Randa], Ali, H.A.[Hesham Arafat], Abdel-Kader, R.F.[Rabab Farouk],
A low-light image enhancement method based on bright channel prior and maximum colour channel,
IET-IPR(15), No. 8, 2021, pp. 1759-1772.
DOI Link 2106
BibRef

Parihar, A.S.[Anil Singh], Singh, K.[Kavinder], Rohilla, H.[Hrithik], Asnani, G.[Gul],
Fusion-based simultaneous estimation of reflectance and illumination for low-light image enhancement,
IET-IPR(15), No. 7, 2021, pp. 1410-1423.
DOI Link 2106
BibRef

Li, F.[Fei], Zheng, J.B.[Jiang-Bin], Zhang, Y.F.[Yuan-Fang],
Generative Adversarial Network for Low-Light Image Enhancement,
IET-IPR(15), No. 7, 2021, pp. 1542-1552.
DOI Link 2106
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

Ma, C.X.[Cheng-Xu], Li, D.H.[Dai-Hui], Zeng, S.Y.[Shang-You], Zhao, J.[Junbo], Chen, H.Y.[Hong-Yang],
An efficient framework for deep learning-based light-defect image enhancement,
IET-IPR(15), No. 7, 2021, pp. 1553-1566.
DOI Link 2106
BibRef

Rao, N.[Ning], Lu, T.[Tao], Zhou, Q.[Qiang], Zhang, Y.D.[Yan-Duo], Wang, Z.Y.[Zhong-Yuan],
Seeing in the Dark by Component-GAN,
SPLetters(28), 2021, pp. 1250-1254.
IEEE DOI 2107
Image reconstruction, Lighting, Generative adversarial networks, Visualization, Generators, Image color analysis, Noise reduction, low-light image BibRef

Khan, R.[Rizwan], Yang, Y.[You], Liu, Q.[Qiong], Qaisar, Z.H.[Zahid Hussain],
A ghostfree contrast enhancement method for multiview images without depth information,
JVCIR(78), 2021, pp. 103175.
Elsevier DOI 2107
Multi-view low-light images, Feature matching, Exposure fusion 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

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

Huang, Z.X.[Zhi-Xiong], Li, J.J.[Jin-Jiang], Hua, Z.[Zhen],
Underwater image enhancement via LBP-based attention residual network,
IET-IPR(16), No. 1, 2022, pp. 158-175.
DOI Link 2112
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

Li, J.Q.[Jia-Qian], Li, J.C.[Jun-Cheng], Fang, F.M.[Fa-Ming], Li, F.[Fang], Zhang, G.X.[Gui-Xu],
Luminance-Aware Pyramid Network for Low-Light Image Enhancement,
MultMed(23), 2021, pp. 3153-3165.
IEEE DOI 2109
Feature extraction, Image enhancement, Lighting, Task analysis, Computer architecture, Image color analysis, pyramid structure BibRef

He, L.[Lei], Long, W.[Wei], Liu, S.X.[Shou-Xin], Li, Y.Y.[Yan-Yan], Ding, W.[Wei],
A night low-illumination image enhancement model based on small probability area filtering and lossless mapping enhancement,
IET-IPR(15), No. 13, 2021, pp. 3221-3238.
DOI Link 2110
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

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

Malik, S.[Sameer], Soundararajan, R.[Rajiv],
A low light natural image statistical model for joint contrast enhancement and denoising,
SP:IC(99), 2021, pp. 116433.
Elsevier DOI 2111
Gaussian scale mixture models, Natural scene statistics, Contrast enhancement, Low light enhancement, Denoising BibRef

Lu, K.[Kun], Zhang, L.[Lihong],
TBEFN: A Two-Branch Exposure-Fusion Network for Low-Light Image Enhancement,
MultMed(23), 2021, pp. 4093-4105.
IEEE DOI 2112
Lighting, Noise reduction, Image enhancement, Estimation, Image reconstruction, Image color analysis, Visualization, transfer function estimation BibRef

Lim, S.[Seokjae], Kim, W.J.[Won-Jun],
DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement,
MultMed(23), 2021, pp. 4272-4284.
IEEE DOI 2112
Laplace equations, Image restoration, Lighting, Image enhancement, Visualization, Image color analysis, Histograms, decomposition-based scheme BibRef

Qiu, Y.S.[Yan-Sheng], Chen, J.[Jun], Wang, Z.[Zheng], Wang, X.[Xiao], Lin, C.W.[Chia-Wen],
Spatio-Spectral Feature Fusion for Low-Light Image Enhancement,
SPLetters(28), 2021, pp. 2157-2161.
IEEE DOI 2112
Frequency-domain analysis, Convolution, Image enhancement, Discrete wavelet transforms, Training, Image color analysis, spatio-spectral fusion BibRef

Karadeniz, A.S.[Ahmet Serdar], Erdem, E.[Erkut], Erdem, A.[Aykut],
Burst Photography for Learning to Enhance Extremely Dark Images,
IP(30), 2021, pp. 9372-9385.
IEEE DOI 2112
Photography, Image color analysis, Pipelines, Computer architecture, Network architecture, Noise measurement, burst images 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

Kim, W.J.[Won-Jun],
Low-light image enhancement by diffusion pyramid with residuals,
JVCIR(81), 2021, pp. 103364.
Elsevier DOI 2112
Low-light image enhancement, Scene illumination, Diffusion pyramid with residuals BibRef

Al Sobbahi, R.[Rayan], Tekli, J.[Joe],
Low-Light Homomorphic Filtering Network for integrating image enhancement and classification,
SP:IC(100), 2022, pp. 116527.
Elsevier DOI 2112
Image enhancement, Low-light conditions, Deep learning, Object classification, Homomorphic filtering BibRef

Al Sobbahi, R.[Rayan], Tekli, J.[Joe],
Low-Light Image Enhancement Using Image-to-Frequency Filter Learning,
CIAP22(II:693-705).
Springer DOI 2205
BibRef

Hu, Y.[Yang], Chen, J.[Jin], Cao, X.[Xin], Chen, X.H.[Xue-Hong], Cui, X.[Xihong], Gan, L.[Liqin],
Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data,
GeoRS(60), 2022, pp. 1-11.
IEEE DOI 2112
Urban areas, Indexes, Data models, Vegetation mapping, Sensors, Satellite broadcasting, Calibration, stable light BibRef

Zhang, Y.[Yu], Di, X.G.[Xiao-Guang], Zhang, B.[Bin], Ji, R.H.[Rui-Hang], Wang, C.H.[Chun-Hui],
Better Than Reference in Low-Light Image Enhancement: Conditional Re-Enhancement Network,
IP(31), 2022, pp. 759-772.
IEEE DOI 2201
Image color analysis, Brightness, Colored noise, Image enhancement, Training, Distortion, Noise reduction, Low-light image, color correction BibRef

Zhang, N.[Ning], Nex, F.[Francesco], Kerle, N.[Norman], Vosselman, G.[George],
LISU: Low-light indoor scene understanding with joint learning of reflectance restoration,
PandRS(183), 2022, pp. 470-481.
Elsevier DOI 2201
BibRef
Earlier:
Towards Learning Low-light Indoor Semantic Segmentation With Illumination-invariant Features,
ISPRS21(B2-2021: 427-432).
DOI Link 2201
Semantic segmentation, Deep learning, Intrinsic image decomposition, Low-light BibRef

Ko, S.[Seonggwan], Park, J.[Jinsun], Chae, B.[Byungjoo], Cho, D.[Donghyeon],
Learning Lightweight Low-Light Enhancement Network Using Pseudo Well-Exposed Images,
SPLetters(29), 2022, pp. 289-293.
IEEE DOI 2202
Training, Feature extraction, Knowledge engineering, Image enhancement, Lighting, Dynamic range, Computational modeling, knowledge distillation BibRef

Huang, H.F.[Hao-Feng], Yang, W.H.[Wen-Han], Hu, Y.Y.[Yue-Yu], Liu, J.Y.[Jia-Ying], Duan, L.Y.[Ling-Yu],
Towards Low Light Enhancement With RAW Images,
IP(31), 2022, pp. 1391-1405.
IEEE DOI 2202
Benchmark testing, Training, Pipelines, Image processing, Lighting, Image enhancement, Histograms, Low-light enhancement, benchmark, factorized enhancement model BibRef

Chang, M.[Meng], Feng, H.J.[Hua-Jun], Xu, Z.H.[Zhi-Hai], Li, Q.[Qi],
Low-Light Image Restoration With Short- and Long-Exposure Raw Pairs,
MultMed(24), 2022, pp. 702-714.
IEEE DOI 2202
Imaging, Image color analysis, Colored noise, Task analysis, Pipelines, Noise reduction, Mobile handsets, Deblurring, denoising, low-light imaging BibRef

Zhao, Z.[Zunjin], 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, Electronics packaging, Image enhancement, Task analysis, Histograms, Cameras, Low-light image enhancement, zero-reference BibRef

Anitha, C., Kumar, R.M.S.[R. Mathusoothana S.],
GEVE: A generative adversarial network for extremely dark image/video enhancement,
PRL(155), 2022, pp. 159-164.
Elsevier DOI 2203
Deep learning, Dynamic range, Generative adversarial networks BibRef

Lu, B.[Bibo], Pang, Z.[Zebang], Gu, Y.[Yanan], Zheng, Y.[Yanmei],
Channel splitting attention network for low-light image enhancement,
IET-IPR(16), No. 5, 2022, pp. 1403-1414.
DOI Link 2203
BibRef

Lu, Y.C.[Yu-Cheng], Jung, S.W.[Seung-Won],
Progressive Joint Low-Light Enhancement and Noise Removal for Raw Images,
IP(31), 2022, pp. 2390-2404.
IEEE DOI 2203
Noise reduction, Image color analysis, Colored noise, Cameras, Lighting, Estimation, Task analysis, Convolutional neural network, low-light image enhancement BibRef

Chen, L.L.[Liang-Liang], Guo, L.[Lin], Cheng, D.Q.[De-Qiang], Kou, Q.Q.[Qi-Qi],
Structure-Preserving and Color-Restoring Up-Sampling for Single Low-Light Image,
CirSysVideo(32), No. 4, April 2022, pp. 1889-1902.
IEEE DOI 2204
Lighting, Image reconstruction, Image color analysis, Training, Reflectivity, Learning systems, Task analysis, Low-light, color-restoring BibRef

Shen, L.[Liran], Ma, Z.Y.[Zhi-Yuan], Er, M.J.[Meng Joo], Fan, Y.S.[Yun-Sheng], Yin, Q.[Qingbo],
Blind Adaptive Structure-Preserving Imaging Enhancement for Low-Light Condition,
SPLetters(29), 2022, pp. 917-921.
IEEE DOI 2205
Lighting, Reflectivity, Adaptation models, Low-pass filters, Image enhancement, Computational complexity, Estimation, retinex model BibRef

Shi, Y.M.[Yang-Ming], Wang, B.Q.[Bin-Quan], Wu, X.[Xiaopo], Zhu, M.[Ming],
Unsupervised Low-Light Image Enhancement by Extracting Structural Similarity and Color Consistency,
SPLetters(29), 2022, pp. 997-1001.
IEEE DOI 2205
Image color analysis, Feature extraction, Training, Optimization, Signal processing algorithms, Brightness, Lighting, unsupervised learning BibRef

Yang, S.L.[Shao-Liang], Zhou, D.M.[Dong-Ming], Cao, J.[Jinde], Guo, Y.[Yanbu],
Rethinking Low-Light Enhancement via Transformer-GAN,
SPLetters(29), 2022, pp. 1082-1086.
IEEE DOI 2205
Transformers, Feature extraction, Generators, Training, Task analysis, Lighting, Image reconstruction, Vision transformer, low light enhancement BibRef

Zhao, J.[Junbo], Chen, H.Y.[Hong-Yang], Zeng, S.[Shangyou], Ma, C.X.[Cheng-Xu],
RISSNet: Retain low-light image details and improve the structural similarity net,
IET-IPR(16), No. 7, 2022, pp. 1793-1806.
DOI Link 2205
BibRef

Lu, Y.[Yuxu], Guo, Y.[Yu], Liu, R.W.[Ryan Wen], Ren, W.Q.[Wen-Qi],
MTRBNet: Multi-Branch Topology Residual Block-Based Network for Low-Light Enhancement,
SPLetters(29), 2022, pp. 1127-1131.
IEEE DOI 2205
Topology, Network topology, Convolution, Visualization, Image enhancement, Brightness, Lighting, Image enhancement, multi-branch topology BibRef

Dhara, S.K.[Sobhan Kanti], Sen, D.[Debashis],
Exposedness-Based Noise-Suppressing Low-Light Image Enhancement,
CirSysVideo(32), No. 6, June 2022, pp. 3438-3451.
IEEE DOI 2206
Lighting, Image enhancement, Estimation, Computational modeling, Atmospheric modeling, Noise reduction, Light scattering, noise suppression BibRef

Wang, W.C.[Wen-Cheng], Chen, Z.[Zhenxue], Yuan, X.H.[Xiao-Hui],
Simple low-light image enhancement based on Weber-Fechner law in logarithmic space,
SP:IC(106), 2022, pp. 116742.
Elsevier DOI 2206
Logarithmic transformation, Low-light image, Non-uniform illumination, Weber-Fechner law, Color compensation BibRef

Li, C.[Chongyi], Guo, C.[Chunle], Loy, C.C.[Chen Change],
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation,
PAMI(44), No. 8, August 2022, pp. 4225-4238.
IEEE DOI 2207
Lighting, Estimation, Training, Image enhancement, Image color analysis, Dynamic range, Task analysis, zero-reference learning BibRef

Guo, C.[Chunle], Li, C.Y.[Chong-Yi], Guo, J.C.[Ji-Chang], Loy, C.C.[Chen Change], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], Cong, R.M.[Run-Min],
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement,
CVPR20(1777-1786)
IEEE DOI 2008
Lighting, Estimation, Dynamic range, Training, Image color analysis, Task analysis, Image enhancement BibRef

Huang, C.Y.[Chao-Yan], Fang, Y.Y.[Ying-Ying], Wu, T.T.[Ting-Ting], Zeng, T.Y.[Tie-Yong], Zeng, Y.H.[Yong-Hua],
Quaternion Screened Poisson Equation for Low-Light Image Enhancement,
SPLetters(29), 2022, pp. 1417-1421.
IEEE DOI 2207
Quaternions, Image color analysis, Color, Mathematical models, Image enhancement, Task analysis, Poisson equations, screened Poisson equation BibRef

Chang, J.[Jie], Zhu, G.P.[Guo-Pu], Zhang, H.L.[Hong-Li], Zhu, L.Y.[Ling-Yu], Yang, W.H.[Wen-Han], Chen, B.L.[Bao-Liang], Lu, F.B.[Fang-Bo], Wang, S.Q.[Shi-Qi],
Enlightening Low-Light Images With Dynamic Guidance for Context Enrichment,
CirSysVideo(32), No. 8, August 2022, pp. 5068-5079.
IEEE DOI 2208
Lighting, Image color analysis, Feature extraction, Image enhancement, Histograms, Image edge detection, contextual feature BibRef

Nie, T.[Ting], Wang, X.F.[Xiao-Feng], Liu, H.X.[Hong-Xing], Li, M.X.[Ming-Xuan], Nong, S.[Shenkai], Yuan, H.[Hangfei], Zhao, Y.C.[Yu-Chen], Huang, L.[Liang],
Enhancement and Noise Suppression of Single Low-Light Grayscale Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Xu, K.[Kai], Chen, H.A.[Huai-An], Xu, C.M.[Chun-Mei], Jin, Y.[Yi], Zhu, C.G.[Chan-Gan],
Structure-Texture Aware Network for Low-Light Image Enhancement,
CirSysVideo(32), No. 8, August 2022, pp. 4983-4996.
IEEE DOI 2208
Image enhancement, Image color analysis, Image edge detection, Task analysis, Lighting, Distortion, Visualization, hybrid loss 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

Wang, Y.[Ya'nan], Jiang, Z.Q.[Zhu-Qing], Liu, C.[Chang], Li, K.[Kai], Men, A.D.[Ai-Dong], Wang, H.Y.[Hai-Ying], Chen, X.B.[Xiao-Bo],
Shedding light on images: Multi-level image brightness enhancement guided by arbitrary references,
PR(131), 2022, pp. 108867.
Elsevier DOI 2208
Low-light image enhancement, Multi-level mapping, Arbitrary references, Codec network, Decomposition, Concatenation BibRef


Qi, J.Z.[Jing-Zhong], Qi, N.[Na], Zhu, Q.[Qing],
SUnet++: Joint Demosaicing and Denoising of Extreme Low-Light Raw Image,
MMMod22(II:171-181).
Springer DOI 2203
BibRef

Jin, S.[Shuang], Qi, N.[Na], Zhu, Q.[Qing], Ouyang, H.R.[Hao-Ran],
Progressive GAN-Based Transfer Network for Low-Light Image Enhancement,
MMMod22(II:292-304).
Springer DOI 2203
BibRef

Cui, Z.T.[Zi-Teng], Qi, G.J.[Guo-Jun], Gu, L.[Lin], You, S.[Shaodi], Zhang, Z.H.[Zeng-Hui], Harada, T.[Tatsuya],
Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection,
ICCV21(2533-2542)
IEEE DOI 2203

WWW Link. Manifolds, Visualization, Computational modeling, Signal processing algorithms, Lighting, Object detection, Machine learning architectures and formulations BibRef

Wang, R.X.[Rui-Xing], Xu, X.G.[Xiao-Gang], Fu, C.W.[Chi-Wing], Lu, J.B.[Jiang-Bo], Yu, B.[Bei], Jia, J.Y.[Jia-Ya],
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment,
ICCV21(9680-9689)
IEEE DOI 2203
Measurement, Mechatronics, Robot vision systems, Dynamics, Noise reduction, Lighting, Process control, Computational photography BibRef

Wang, K.[Kun], Zhang, Z.Y.[Zhen-Yu], Yan, Z.Q.[Zhi-Qiang], Li, X.[Xiang], Xu, B.[Baobei], Li, J.[Jun], Yang, J.[Jian],
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark,
ICCV21(16035-16044)
IEEE DOI 2203
Training, Codes, Annotations, Brightness, Estimation, Lighting, Scene analysis and understanding, Vision for robotics and autonomous vehicles BibRef

Song, W.Z.[Wen-Zheng], Suganuma, M.[Masanori], Liu, X.[Xing], Shimobayashi, N.[Noriyuki], Maruta, D.[Daisuke], Okatani, T.[Takayuki],
Matching in the Dark: A Dataset for Matching Image Pairs of Low-light Scenes,
ICCV21(6009-6018)
IEEE DOI 2203
Image sensors, Visualization, Simultaneous localization and mapping, Image matching, Datasets and evaluation BibRef

Zheng, C.J.[Chuan-Jun], Shi, D.M.[Da-Ming], Shi, W.[Wentian],
Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement,
ICCV21(4419-4428)
IEEE DOI 2203
Adaptation models, Adaptive systems, TV, Noise reduction, Pipelines, Estimation, Minimization, Low-level and physics-based vision, BibRef

Kim, B.[Bomi], Lee, S.[Sunhyeok], Kim, N.[Nahyun], Jang, D.G.[Dong-Gon], Kim, D.S.[Dae-Shik],
Learning Color Representations for Low-Light Image Enhancement,
WACV22(904-912)
IEEE DOI 2202
Training, Representation learning, Histograms, Visualization, Image color analysis, Supervised learning, Image restoration, Image Processing -> Image Restoration Deep Learning BibRef

Lamba, M.[Mohit], Mitra, K.[Kaushik],
Fast and Efficient Restoration of Extremely Dark Light Fields,
WACV22(3152-3161)
IEEE DOI 2202
Geometry, Fuses, Estimation, Light fields, Distance measurement, Image and Video Synthesis Low-level and Physics-based Vision BibRef

Zheng, S.[Shen], Gupta, G.[Gaurav],
Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement,
RWSurvil22(581-590)
IEEE DOI 2202
Photography, Image segmentation, Convolution, Motion segmentation, Conferences, Semantics BibRef

Akai, M.[Masato], Ueda, Y.[Yoshiaki], Koga, T.[Takanori], Suetake, N.[Noriaki],
A Single Backlit Image Enhancement Method for Improvement of Visibility of Dark Part,
ICIP21(1659-1663)
IEEE DOI 2201
Histograms, Task analysis, Image enhancement, Backlit image, image enhancement, weight map, alpha blending BibRef

Zhao, L.C.[Ling-Chao], Gong, X.L.[Xiao-Lin], Liu, K.[Kaihua], Wang, J.[Jian], Zhao, B.[Bai], Liu, Y.[Yu],
Color Channel Fusion Network for Low-Light Image Enhancement,
ICIP21(1654-1658)
IEEE DOI 2201
Training, Image color analysis, Lighting, Image reconstruction, Image enhancement, low-light image enhancement, deep learning, detail enhancement BibRef

Guo, L.Q.[Lan-Qing], Wan, R.J.[Ren-Jie], Su, G.M.[Guan-Ming], Kot, A.C.[Alex C.], Wen, B.[Bihan],
Multi-Scale Feature Guided Low-Light Image Enhancement,
ICIP21(554-558)
IEEE DOI 2201
Visualization, Inverse problems, Lighting, Object detection, Feature extraction, Generative adversarial networks, Low-Light, Unsupervised Learning BibRef

Ji, Z.[Zhe], Jung, C.[Cheolkon],
Subband Adaptive Enhancement of Low Light Images Using Wavelet-Based Convolutional Neural Networks,
ICIP21(1669-1673)
IEEE DOI 2201
Visualization, Adaptive systems, Noise reduction, Redundancy, Discrete wavelet transforms, Convolutional neural networks, wavelet. BibRef

Tang, P.L.[Peng-Liang], Guo, X.Q.[Xiao-Qiang], Ju, G.D.[Guo-Dong], Shen, L.H.[Liang-Heng], Men, A.[Aidong],
Integration-and-Diffusion Network for Low-Light Image Enhancement,
ICIP21(1664-1668)
IEEE DOI 2201
Learning systems, Image color analysis, Image enhancement, Image reconstruction, Signal to noise ratio, Photonics, color recovery BibRef

Li, C.X.[Cheng-Xi], Qu, X.Y.[Xiang-Yu], Gnanasambandam, A.[Abhiram], Elgendy, O.A.[Omar A.], Ma, J.J.[Jia-Ju], Chan, S.H.[Stanley H.],
Photon-Limited Object Detection using Non-local Feature Matching and Knowledge Distillation,
LCI21(3959-3970)
IEEE DOI 2112
Performance evaluation, Night vision, Surveillance, Microscopy, Object detection, Detectors, Feature extraction BibRef

Zhou, C.[Chu], Teng, M.[Minggui], Han, J.[Jin], Xu, C.[Chao], Shi, B.X.[Bo-Xin],
DeLiEve-Net: Deblurring Low-light Images with Light Streaks and Local Events,
PBDL21(1155-1164)
IEEE DOI 2112
Neural networks, Dynamic range, Cameras, Spatial resolution, Low latency communication 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

Xiong, J.H.[Jin-Hui], Wang, J.[Jian], Heidrich, W.[Wolfgang], Nayar, S.[Shree],
Seeing in Extra Darkness Using a Deep-Red Flash,
CVPR21(9995-10004)
IEEE DOI 2111
Training, Photography, Sensitivity, Modulation, Prototypes, Cameras, Pattern recognition BibRef

Xia, Z.H.[Zhi-Hao], Gharbi, M.[Michaël], Perazzi, F.[Federico], Sunkavalli, K.[Kalyan], Chakrabarti, A.[Ayan],
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments,
CVPR21(2063-2072)
IEEE DOI 2111
Photography, Visualization, Image color analysis, Noise reduction, Lighting, Rendering (computer graphics), Surface texture BibRef

Wang, W.J.[Wen-Jing], Yang, W.H.[Wen-Han], Liu, J.Y.[Jia-Ying],
HLA-Face: Joint High-Low Adaptation for Low Light Face Detection,
CVPR21(16190-16199)
IEEE DOI 2111
Training, Annotations, Face recognition, Surveillance, Pipelines, Buildings, Detectors BibRef

Zhang, F.[Fan], Li, Y.[Yu], You, S.[Shaodi], Fu, Y.[Ying],
Learning Temporal Consistency for Low Light Video Enhancement from Single Images,
CVPR21(4965-4974)
IEEE DOI 2111
Training, Computational modeling, Video sequences, Stability analysis, Planning, Pattern recognition BibRef

Lamba, M.[Mohit], Mitra, K.[Kaushik],
Restoring Extremely Dark Images in Real Time,
CVPR21(3486-3496)
IEEE DOI 2111
Training, Deep learning, Image resolution, Computational modeling, Graphics processing units, Object detection, Cameras BibRef

Sharma, A.[Aashish], Tan, R.T.[Robby T.],
Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects,
CVPR21(11972-11981)
IEEE DOI 2111
Dynamic range, Semisupervised learning, Cameras, Pattern recognition, Low-frequency noise 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, Computer architecture, Search problems, Pattern recognition BibRef

Moseley, B.[Ben], Bickel, V.[Valentin], López-Francos, I.G.[Ignacio G.], Rana, L.[Loveneesh],
Extreme Low-Light Environment-Driven Image Denoising over Permanently Shadowed Lunar Regions with a Physical Noise Model,
CVPR21(6313-6323)
IEEE DOI 2111
Training, Solid modeling, Moon, Noise reduction, Training data, Ray tracing BibRef

Qiu, Y.S.[Yan-Sheng], Chen, J.[Jun], Wang, X.[Xiao], Jang, K.[Kui],
Illuminate Low-light Image via Coarse-to-fine Multi-level Network,
MMMod21(I:253-264).
Springer DOI 2106
BibRef

Wang, L.W.[Li-Wen], Siu, W.C.[Wan-Chi], Liu, Z.S.[Zhi-Song], Li, C.T.[Chu-Tak], Lun, D.P.K.[Daniel Pak-Kong],
Video Lightening with Dedicated CNN Architecture,
ICPR21(6447-6454)
IEEE DOI 2105
Measurement, Legged locomotion, Visualization, Uncertainty, Roads, Transfer learning, Lighting, Low-light video enhancement, deep learning BibRef

Guo, H.F.[Hai-Feng], Lu, T.[Tong], Wu, Y.[Yirui],
Dynamic Low-Light Image Enhancement for Object Detection via End-to-End Training,
ICPR21(5611-5618)
IEEE DOI 2105
Training, Image quality, Lighting, Object detection, Detectors, Pattern recognition, Low-Light Image Enhancement, Object Detection BibRef

Song, Y.[Yuda], Zhu, Y.[Yunfang], Du, X.[Xin],
Automatical Enhancement and Denoising of Extremely Low-light Images,
ICPR21(858-865)
IEEE DOI 2105
Histograms, Noise reduction, Neural networks, Lighting, Manuals, Image sampling, Image restoration BibRef

Vogt, C.[Carson], Lyu, G.[Geng], Subr, K.[Kartic],
Lightless Fields: Enhancement and Denoising of Light-deficient Light Fields,
ISVC20(I:383-396).
Springer DOI 2103
BibRef

Guo, P.[Peiyao], Ma, Z.[Zhan],
Low-light Color Imaging via Dual Camera Acquisition,
ACCV20(II:150-167).
Springer DOI 2103
BibRef

Loh, Y.P.[Yuen Peng],
Exploring the Contributions of Low-light Image Enhancement to Network-based Object Detection,
MOI2QDN20(655-669).
Springer DOI 2103
BibRef

Zhou, Y., Wang, R., Zhao, Y.,
A night-time outdoor data set for low-light enhancement,
VCIP20(507-510)
IEEE DOI 2102
Training, Lighting, Image color analysis, Feature extraction, Dynamic range, Task analysis, Image enhancement, data set BibRef

Azizi, Z., Lei, X., Kuo, C.C.J.,
Noise-Aware Texture-Preserving Low-Light Enhancement,
VCIP20(443-446)
IEEE DOI 2102
Lighting, Noise reduction, Noise measurement, Image color analysis, Image edge detection, Optimization, Estimation, denoising BibRef

Ngee Bow, N.C., Tran, V.H., Kerdsiri, P., Loh, Y.P., Huang, C.C.,
DEN: Disentanglement and Enhancement Networks for Low Illumination Images,
VCIP20(419-422)
IEEE DOI 2102
Lighting, Feature extraction, Reflectivity, Image enhancement, Finite element analysis, Convolution, Tuning, multi-branch enhancement network BibRef

Xi, L., Chen, W., Zhao, C., Wu, X., Wang, J.,
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment,
FG20(1-7)
IEEE DOI 2102
Gesture recognition, Face recognition BibRef

Triantafyllidou, D.[Danai], Moran, S.[Sean], McDonagh, S.[Steven], Parisot, S.[Sarah], Slabaugh, G.[Gregory],
Low Light Video Enhancement Using Synthetic Data Produced with an Intermediate Domain Mapping,
ECCV20(XIII:103-119).
Springer DOI 2011
BibRef

Zheng, J., Jung, C., Yu, S.,
Low Light Image Enhancement by Multispectral Fusion of RGB and NIR Images,
ICIP20(2541-2545)
IEEE DOI 2011
Reliability, Image color analysis, Colored noise, Imaging, Noise reduction, Linear systems, Image enhancement, Image fusion, total variation BibRef

Chi, Y.H.[Yi-Heng], Gnanasambandam, A.[Abhiram], Koltun, V.[Vladlen], Chan, S.H.[Stanley H.],
Dynamic Low-light Imaging with Quanta Image Sensors,
ECCV20(XXI:122-138).
Springer DOI 2011
BibRef

Ke, X.[Xue], Lin, W.[Wei], Chen, G.J.[Gao-Jie], Chen, Q.[Quan], Qi, X.Z.[Xian-Zhi], Ma, J.[Jie],
EDLLIE-Net: Enhanced Deep Convolutional Networks for Low-Light Image Enhancement,
ICIVC20(59-68)
IEEE DOI 2009
Image enhancement, Task analysis, Feature extraction, Lighting, Image color analysis, Artificial intelligence, Automation, EDLLIE-Net BibRef

Xu, K., Yang, X., Yin, B., Lau, R.W.H.,
Learning to Restore Low-Light Images via Decomposition-and-Enhancement,
CVPR20(2278-2287)
IEEE DOI 2008
Noise measurement, Image color analysis, Colored noise, Image enhancement, Lighting, Integrated circuit modeling, Noise reduction BibRef

Wei, K., Fu, Y., Yang, J., Huang, H.,
A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising,
CVPR20(2755-2764)
IEEE DOI 2008
Data models, Photonics, Semiconductor device modeling, Cameras, Noise measurement, Pipelines BibRef

Atoum, Y., Ye, M., Ren, L., Tai, Y., Liu, X.,
Color-wise Attention Network for Low-light Image Enhancement,
NTIRE20(2130-2139)
IEEE DOI 2008
Image color analysis, Noise reduction, Task analysis, Colored noise, Lighting, Frequency modulation, Computer vision BibRef

Liba, O., Movshovitz-Attias, Y., Cai, L., Pritch, Y., Tsai, Y., Chen, H., Eban, E., Barron, J.T.,
Sky Optimization: Semantically aware image processing of skies in low-light photography,
NTIRE20(2230-2238)
IEEE DOI 2008
Image segmentation, Image resolution, Mobile handsets, Neural networks, Cameras, Image color analysis, Colored noise BibRef

Jiang, H.Y.[Hai-Yang], Zheng, Y.Q.[Yin-Qiang],
Learning to See Moving Objects in the Dark,
ICCV19(7323-7332)
IEEE DOI 2004
convolutional neural nets, image colour analysis, image enhancement, image motion analysis, BibRef

Chen, C.[Chen], Chen, Q.F.[Qi-Feng], Do, M.[Minh], Koltun, V.[Vladlen],
Seeing Motion in the Dark,
ICCV19(3184-3193)
IEEE DOI 2004
image classification, image representation, image resolution, image segmentation, image sensors, Noise measurement BibRef

Wang, W., Chen, X., Yang, C., Li, X., Hu, X., Yue, T.,
Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise,
ICCV19(4110-4118)
IEEE DOI 2004
cameras, image capture, image denoising, image enhancement, recurrent neural nets, video signal processing, digital cameras, Gaussian distribution BibRef

Wang, R.X.[Rui-Xing], Zhang, Q.[Qing], Fu, C.W.[Chi-Wing], Shen, X.Y.[Xiao-Yong], Zheng, W.S.[Wei-Shi], Jia, J.Y.[Jia-Ya],
Underexposed Photo Enhancement Using Deep Illumination Estimation,
CVPR19(6842-6850).
IEEE DOI 2002
BibRef

Kanellakis, C.[Christoforos], Karvelis, P.[Petros], Nikolakopoulos, G.[George],
Image Enhancing in Poorly Illuminated Subterranean Environments for Mav Applications: A Comparison Study,
CVS19(511-520).
Springer DOI 1912
BibRef

Cheng, Y., Yan, J., Wang, Z.,
Enhancement of Weakly Illuminated Images by Deep Fusion Networks,
ICIP19(924-928)
IEEE DOI 1910
weakly illuminated image enhancement, Retinex, image fusion, convolution neural network BibRef

Kim, G., Kwon, D., Kwon, J.,
Low-Lightgan: Low-Light Enhancement Via Advanced Generative Adversarial Network With Task-Driven Training,
ICIP19(2811-2815)
IEEE DOI 1910
Low-light enhancement, task-driven training set, GANs, spectral normalization BibRef

Ghosh, S., Chaudhury, K.N.,
Kernel-Based Image Filtering: Fast Algorithms and Applications,
ICIP19(3018-3019)
IEEE DOI 1910
low-light enhancement, retinex, bilateral filter, Fourier approximation, fast algorithm BibRef

Ghosh, S., Chaudhury, K.N.,
Fast Bright-Pass Bilateral Filtering for Low-Light Enhancement,
ICIP19(205-209)
IEEE DOI 1910
low-light enhancement, retinex, bilateral filter, Fourier approximation, fast algorithm BibRef

Malik, S., Soundararajan, R.,
A Model Learning Approach For Low Light Image Restoration,
ICIP20(1033-1037)
IEEE DOI 2011
BibRef
Earlier:
Llrnet: A Multiscale Subband Learning Approach for Low Light Image Restoration,
ICIP19(779-783)
IEEE DOI 1910
Image restoration, Noise reduction, Noise measurement, Distortion, Computational modeling, Training, Cameras, Contrast enhancement, CNN. Contrast enhancement, low light enhancement, denoising. Laplacian pyramid BibRef

Dhara, S.K., Sen, D.,
Low Light Image Enhancement Using Grover's Algorithm on Superposed Luminance Levels,
ICIP18(1113-1117)
IEEE DOI 1809
Histograms, Image enhancement, Lighting, Image color analysis, Probability density function, Proposals, Quantum probability BibRef

Rupapara, P., Rangavajjula, A., Jain, A.,
Low complexity image fusion in bayer domain using a monochrome sensor and bayer sensor,
ICIP17(1980-1984)
IEEE DOI 1803
Erbium, Bayer image fusion, Dual sensor system, Low light photography BibRef

Tao, L.[Li], Zhu, C.[Chuang], Xiang, G.Q.[Guo-Qing], Li, Y.[Yuan], Jia, H.Z.[Hui-Zhu], Xie, X.D.[Xiao-Dong],
LLCNN: A Convolutional Neural Network for Low-Light Image Enhancement,
VCIP17(1-4)
IEEE DOI 1804
convolution, feature extraction, feedforward neural nets, image enhancement, image texture, CNN based method, low-light image BibRef

Tao, L., Zhu, C., Song, J., Lu, T., Jia, H., Xie, X.,
Low-light image enhancement using CNN and bright channel prior,
ICIP17(3215-3219)
IEEE DOI 1803
Adaptation models, Atmospheric modeling, Filtering theory, Image color analysis, Mathematical model, Scattering, CNN, low-light model BibRef

Shi, W., Chen, C., Jiang, F., Zhao, D., Shen, W.,
Group-based sparse representation for low lighting image enhancement,
ICIP16(4082-4086)
IEEE DOI 1610
Decision support systems BibRef

Kim, Y.[Youngbae], Koh, Y.J.[Yeong Jun], Lee, C.[Chulwoo], Kim, S.H.[Se-Hoon], Kim, C.S.[Chang-Su],
Dark image enhancement based onpairwise target contrast and multi-scale detail boosting,
ICIP15(1404-1408)
IEEE DOI 1512
Dark image enhancement BibRef

Yang, J., Jiang, X., Pan, C., Liu, C.L.[Cheng-Lin],
Enhancement of Low Light Level Images with coupled dictionary learning,
ICPR16(751-756)
IEEE DOI 1705
Brightness, Clustering algorithms, Dictionaries, Estimation, Histograms, Image reconstruction, Training BibRef

Arun, M., Rajagopalan, A.N.,
Hand-held low-light photography with exposure bracketing,
ICIP16(1749-1753)
IEEE DOI 1610
Cameras BibRef

Fotiadou, K.[Konstantina], Tsagkatakis, G.[Grigorios], Tsakalides, P.[Panagiotis],
Low Light Image Enhancement via Sparse Representations,
ICIAR14(I: 84-93).
Springer DOI 1410
BibRef

Zhang, X.D.[Xiang-Dong], Shen, P.[Peiyi], Luo, L.[Lingli], Zhang, L.[Liang], Song, J.[Juan],
Enhancement and noise reduction of very low light level images,
ICPR12(2034-2037).
WWW Link. 1302
BibRef

Matsui, S.[Sosuke], Okabe, T.[Takahiro], Shimano, M.[Mihoko], Sato, Y.[Yoichi],
Image Enhancement of Low-Light Scenes with Near-Infrared Flash Images,
ACCV09(I: 213-223).
Springer DOI 0909
BibRef

Mohanty, K.K., Gellaboina, M.K.,
Enhancement of low light image based on Gaussian Mixture Modeling,
EUVIP10(232-236).
IEEE DOI 1110
BibRef

Tao, L.[Li], Ngo, H.[Hau], Zhang, M.[Ming], Livingston, A., Asari, V.K.,
A multisensor image fusion and enhancement system for assisting drivers in poor lighting conditions,
AIPR05(106-113).
IEEE DOI 0510

See also Illuminance-Reflectance Model for Nonlinear Enhancement of Color Images, An. BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Sharpening, Unsharp Masking .


Last update:Aug 11, 2022 at 11:48:53