低光照图像增强算法综述

马龙; 马腾宇; 刘日升

doi:10.11834/jig.210852

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低光照图像增强算法综述
The review of low-light image enhancement
2022年27卷第5期页码：1392-1409
收稿：2021-09-17，

修回：2022-1-19，

录用：2022-1-26，

纸质出版：2022-05-16
DOI： 10.11834/jig.210852
稿件说明：

移动端阅览

马龙, 马腾宇, 刘日升. 低光照图像增强算法综述[J]. 中国图象图形学报, 2022,27(5):1392-1409. DOI： 10.11834/jig.210852.

Long Ma, Tengyu Ma, Risheng Liu. The review of low-light image enhancement[J]. Journal of Image and Graphics, 2022, 27(5): 1392-1409. DOI： 10.11834/jig.210852.

摘要

低光照图像增强旨在提高光照不足场景下捕获数据的视觉感知质量以获取更多信息，逐渐成为图像处理领域中的研究热点，在自动驾驶、安防等人工智能相关行业中具有十分广阔的应用前景。传统的低光照图像增强技术往往需要高深的数学技巧以及严格的数学推导，且导出的迭代过程普遍流程复杂，不利于实际应用。随着大规模数据集的相继诞生，基于深度学习的低光照图像增强已经成为当前的主流技术，然而此类技术受限于数据分布，存在性能不稳定、应用场景单一等问题。此外，在低光照环境下的高层视觉任务(如目标检测)对于低光照图像增强技术的发展带来了新的机遇与挑战。本文从3个方面系统地综述了低光照图像增强技术的研究现状。介绍了现有低光照图像数据集，详述了低光照图像增强技术的发展脉络，通过对比低光照图像增强质量与夜间人脸检测精度，进一步对现有低光照增强技术进行了全面评估与分析。基于对上述现状的探讨，结合实际应用，本文指出当前技术的局限性，并对其发展趋势进行预测。

Abstract

Low-light image enhancement aims to improve the visual perception quality of captured data in the context of low-light scenarios. The purpose of low-light image enhancement is to improve the visual quality via image brightness enhancement. Low-light image enhancement is a key factor to low-light face detection and nighttime semantic segmentation.Our systematic and detailed review is focused on the recent development of low-light image enhancement.

We first carry out a comprehensive and systematic analysis for low-light image enhancement on the three aspects as mentioned below: 1) the development of low-light image datasets

2) the development of low-light image enhancement technology

and 3) the experimental evaluation synthesis. Finally

our demonstrated results are summarized and forecasted in related to low-light image enhancement further.First of all

as far as the existing low-light image enhancement data set is concerned

it reveals a trend in the scale of sizes (small to large)

multi-scenarios(solo to diverse)

and data involvement degree(simple to complex). Most of the data sets are attributed to unpaired data

and the target pairwise data sets cannot be effectively synthesized due to the difficulty of illumination in low-light image enhancement modeling. The existing pairs of low illumination image data set labels are mainly subjected to manual parameter settings like the exposure time adjustment or expertise modification).The existing reference images in pairs of data sets have challenged to represent the scene information captured in low-light observation accurately. In addition

the construction process of some data sets is relevant to detection or segmentation labels. It is necessary to establish a connection and explore the impact of low-level visual tasks with high-level visual tasks and faciliate high-level visual tasks like detection and semantic segmentation in a low-light scenario.Second

existing low-light image enhancement techniques can be roughly divided into three categories: 1) distribution-based mapping

2)model-based optimization

and 3) deep learning

respectively. Data-driven deep learning technology has significantly promoted the development of low-light image enhancement. Thanks to the development of the existing low-light image enhancement technology

the traditional model design method has been transformed into data-driven deep learning technology. Among them

It can resolve low-light image enhancement issue based on mapping the value distribution of low-light input to amplify smaller values (displayed as dark)

while exposure unevenness is still the a challenged issue to deal with. The model optimization based methods make assumptions about the ground truth via a priori regular condition designation

and do not depend on the amount of training data

but achieve relatively stable performance through the image analysis itself. The deep learning based image enhancement method is to learn via a large amount of training data and realize image enhancement based on designing a deep network-related/independent of the physical model. The trend moves towards semi-supervised/unsupervised/self-supervised learning mechanisms from fully supervised learning mechanisms and focuses more on image enhancement quality when pairwise ground truth data is not available. However

the loss function design in the training process and the training relies on the design of the loss function and the adjustment of network parameters. The existing enhanced network structure is gradually changing from complex to lightweight. Simultaneously

the improvement of visual quality is related to the running speed of the network. There is a lack of effective indicators that can accurately reflect the enhanced image quality due to the specialty of the low-light image enhancement. Currently

a series of downstream high-level vision tasks have been adopting to evaluate the quality of enhanced images and transferring to user-friendly low-light visual image enhancement to high-level vision task performance priority.Meanwhile

a series of experimental evaluations demonstrate that existing optimized model based methods have better generalization ability than those deep learning based methods

while existing unsupervised learning techniques are more robust and efficient than fully supervised learning methods. It can be obtained from the high-level vision tasks in low-light scenes that low-light enhancement has a certain effect on more tasks although good visual effects has not obtained higher high-level vision task accuracy. This indirect confirmation of the disparity of the visual quality expression is orientated from existing works and high-level visual tasks. It is worth noting that the results obtained of trained networks on paired data lack generality and make it difficult to characterize natural image distributions. The unsupervised method can generate enhanced results to satisfy the natural image distribution via ta natural image distribution related loss function

The following four potential research perspectives are proposed

including 1)the inherent laws issue of low-light images in different scenes and reduce the dependence on paired data to endow the algorithm with scene-independent generalization ability; 2) an efficient network framework construction for low-light image enhancement tasks; 3) an effective learning strategy to make the framework learn completely; and 4) a connection between low-light image enhancement and high-level vision tasks (e.g.

detection).

关键词

Keywords

references

Bennett E P and McMillan L. 2005. Video enhancement using per-pixel virtual exposures//Proceedings of ACM SIGGRAPH 2005 Papers. Los Angeles, California, USA: ACM: 845-852 [ DOI: 10.1145/1186822.1073272 http://dx.doi.org/10.1145/1186822.1073272 ]

Brifman A, Romano Y and Elad M. 2019. Unified single-image and video super-resolution via denoising algorithms. IEEE Transactions on Image Processing, 28(12): 6063-6076 [DOI: 10.1109/TIP.2019.2924173]

Bychkovsky V, Paris S, Chan E and Durand F. 2011. Learning photographic global tonal adjustment with a database of input/output image pairs//Proceedings of 2011 Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE: 97-104 [ DOI: 10.1109/CVPR.2011.5995332 http://dx.doi.org/10.1109/CVPR.2011.5995332 ]

Cai B L, Xu X M, Guo K L, Jia K, Hu B and Tao A C. 2017. Ajoint intrinsic-extrinsic prior model for retinex//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 4020-4029 [ DOI: 10.1109/ICCV.2017.431 http://dx.doi.org/10.1109/ICCV.2017.431 ]

Cai J R, Gu S H and Zhang L. 2018. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4): 2049-2062 [DOI: 10.1109/TIP.2018.2794218]

Chen C, Chen Q F, Do M and Koltun V. 2019. Seeing motion in the dark//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 3185-3194 [ DOI: 10.1109/ICCV.2019.00328 http://dx.doi.org/10.1109/ICCV.2019.00328 ]

Chen C, Chen Q F, Xu J and Koltun V. 2018a. Learning to see in the dark//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 3291-3300 [ DOI: 10.1109/CVPR.2018.00347 http://dx.doi.org/10.1109/CVPR.2018.00347 ]

Chen W, Wang W J, Yang W H and Liu J Y. 2018c. Deep retinex decomposition for low-light enhancement//Proceedings of British Machine Vision Conference 2018. Newcastle, UK: BMVA

Chen Y S, Wang Y C, Kao M H and Chuang Y Y. 2018b. Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 6306-6314 [ DOI: 10.1109/CVPR.2018.00660 http://dx.doi.org/10.1109/CVPR.2018.00660 ]

Cheng H D and Shi X J. 2004. A simple and effective histogram equalization approach to image enhancement. Digital Signal Processing, 14(2): 158-170 [DOI: 10.1016/j.dsp.2003.07.002]

Dai D X and Van Gool L. 2018. Dark model adaptation: semantic image segmentation from daytime to nighttime//Proceedings of the 21st International Conference on Intelligent Transportation Systems. Maui, USA: IEEE: 3819-3824 [ DOI: 10.1109/ITSC.2018.8569387 http://dx.doi.org/10.1109/ITSC.2018.8569387 ]

Fan M H, Wang W J, Yang W H and Liu J Y. 2020. Integrating semantic segmentation and retinex model for low-light image enhancement//Proceedings of the 28th ACM International Conference on Multimedia. Virtual Event (Seattle, WA), USA: ACM: 2317-2325 [ DOI: 10.1145/3394171.3413757 http://dx.doi.org/10.1145/3394171.3413757 ]

Fu X Y, Liao Y H, Zeng D L, Huang Y, Zhang X P and Ding X H. 2015. A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Transactions on Image Processing, 24(12): 4965-4977 [DOI: 10.1109/TIP.2015.2474701]

Fu X Y, Zeng D L, Huang Y, Zhang X P and Ding X H. 2016. A weighted variational model for simultaneous reflectance and illumination estimation//Proceedings of 2016 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 2782-2790 [ DOI: 10.1109/CVPR.2016.304 http://dx.doi.org/10.1109/CVPR.2016.304 ]

Guo C L, Li C Y, Guo J C, Loy C C, Hou J H, Kwong S and Cong R M. 2020. Zero-reference deep curve estimation for low-light image enhancement//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 1777-1786 [ DOI: 10.1109/CVPR42600.2020.00185 http://dx.doi.org/10.1109/CVPR42600.2020.00185 ]

Guo X J, Li Y and Ling H B. 2017. LIME: low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2): 982-993 [DOI: 10.1109/TIP.2016.2639450]

Hai J, Xuan Z, Han S C, Yang R, Hao Y T, Zhou F Z and Lin F. 2021. R2RNet: low-lightimage enhancement via real-low to real-normal network[EB/OL]. [2021-06-28] . https://arxiv.org/pdf/2106.14501.pdf https://arxiv.org/pdf/2106.14501.pdf

Hao S J, Han X, Guo Y R, Xu X and Wang M. 2020. Low-light image enhancement with semi-decoupled decomposition. IEEE Transactions on Multimedia, 22(12): 3025-3038 [DOI: 10.1109/TMM.2020.2969790]

Hasinoff S W, Sharlet D, Geiss R, Adams A, Barron J T, Kainz F, Chen J W and Levoy M. 2016. Burst photography for high dynamic range and low-light imaging on mobile cameras. ACM Transactions on Graphics, 35(6): #192 [DOI: 10.1145/2980179.2980254]

Hu Y M, He H, Xu C X, Wang B Y and Lin S. 2018. Exposure: a white-box photo post-processing framework. ACM Transactions on Graphics, 37(2): #26

Huang H B, Yu A J and He R. 2021. Memory oriented transfer learning for semi-supervised image deraining//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 7728-7737 [ DOI: 10.1109/CVPR46437.2021.00764 http://dx.doi.org/10.1109/CVPR46437.2021.00764 ]

Ignatov A, Kobyshev N, Timofte R and Vanhoey K. 2017. DSLR-quality photos on mobile devices with deep convolutional networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 3297-3305 [ DOI: 10.1109/ICCV.2017.355 http://dx.doi.org/10.1109/ICCV.2017.355 ]

Jiang H Y and Zheng Y Q. 2019. Learning to see moving objects in the dark//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 7324-7333 [ DOI: 10.1109/ICCV.2019.00742 http://dx.doi.org/10.1109/ICCV.2019.00742 ]

Jiang Y F, Gong X Y, Liu D, Cheng Y, Fang C, Shen X H, Yang J C, Zhou P and Wang Z Y. 2021. EnlightenGAN: deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30: 2340-2349 [DOI: 10.1109/TIP.2021.3051462]

Jin D, Ma L, Liu R S and Fan X. 2021. Bridging the gap between low-light scenes: bilevel learning for fast adaptation//Proceedings of the 29th ACM International Conference on Multimedia. [s. l.]: ACM: 2401-2409 [ DOI: 10.1145/3474085.3475404 http://dx.doi.org/10.1145/3474085.3475404 ]

Jobson D J, RahmanZ and Woodell G A. 1997. A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7): 965-976 [DOI: 10.1109/83.597272]

Kim Y T. 1997. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Transactions on Consumer Electronics, 43(1): 1-8 [DOI: 10.1109/30.580378]

Kong X Y, Liu L and Qian Y S. 2021. Low-light image enhancement via Poisson noise aware retinex model. IEEE Signal Processing Letters, 28: 1540-1544 [DOI: 10.1109/LSP.2021.3096160]

Land E H and McCann J J. 1971. Lightness and retinex theory. Journal of the Optical Society of America, 61(1): 1-11 [DOI: 10.1364/JOSA.61.000001]

Lee C, Lee C and Kim C S. 2013. Contrast enhancement based on layered difference representation of 2D histograms. IEEE Transactions on Image Processing, 22(12): 5372-5384 [DOI: 10.1109/TIP.2013.2284059]

Li C Y, Guo C L and Chen C L. 2021b. Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence [DOI: 10.1109/TPAMI.2021.3063604]

Li C Y, Guo C L, Han L H, Jiang J, Cheng M M, Gu J W and Loy C C. 2021a. Low-light image and video enhancement using deep learning: a survey[EB/OL]. [2021-04-21] . https://arxiv.org/pdf/2104.10729.pdf https://arxiv.org/pdf/2104.10729.pdf

Li C Y, Guo J C, Porikli F and Pang Y W. 2018b. LightenNet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recognition Letters, 104: 15-22

Li J, Wang Y B, Wang C A, Tai Y, Qian J J, Yang J, Wang C J, Li J L and Huang F Y. 2019. DSFD: dual shot face detector//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 5060-5069 [ DOI: 10.1109/CVPR.2019.00520 http://dx.doi.org/10.1109/CVPR.2019.00520 ]

Li J Q, Li J C, Fang F M, Li F and Zhang G X. 2021c. Luminance-aware pyramid network for low-light image enhancement. IEEE Transactions on Multimedia, 23: 3153-3165 [DOI: 10.1109/TMM.2020.3021243]

Li M D, Liu J Y, Yang W H, Sun X Y and Guo Z M. 2018a. Structure-revealing low-light image enhancement via robust retinex model. IEEE Transactions on Image Processing, 27(6): 2828-2841 [DOI: 10.1109/TIP.2018.2810539]

Liang J X, Wang J W, Quan Y H, Chen T Y, Liu J Y, Ling H B and Xu Y. 2021. Recurrent exposure generation for low-light face detection. IEEE Transactions on Multimedia: 1609-1621 [DOI: 10.1109/TMM.2021.3068840]

Lim S and Kim W. 2021. DSLR: deep stacked laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23: 4272-4284 [DOI: 10.1109/TMM.2020.3039361]

Liu J Y, Xu D J, Yang W H, Fan M H and Huang H F. 2021a. Benchmarking low-light image enhancement and beyond. International Journal of Computer Vision, 129(4): 1153-1184 [DOI: 10.1007/s11263-020-01418-8]

Liu M Y, Breuel T and Kautz J. 2017. Unsupervised image-to-image translation networks//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc: 700-708

Liu R S, Ma L, Zhang J A, Fan X and Luo Z X. 2021b. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 10561-10570 [ DOI: 10.1109/CVPR46437.2021.01042 http://dx.doi.org/10.1109/CVPR46437.2021.01042 ]

Loh Y P and Chan C S. 2019. Getting to know low-light images with the exclusively dark dataset. Computer Vision and Image Understanding, 178: 30-42 [DOI: 10.1016/j.cviu.2018.10.010]

Lore K G, Akintayo A and Sarkar S. 2017. LLNEt: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61: 650-662

Lyu F F, Liu B and Lu F. 2020. Fast enhancement for non-uniform illumination images using light-weight CNNs//Proceedings of the 28th ACM International Conference on Multimedia. Virtual Event (Seattle, WA), USA: ACM: 1450-1458 [ DOI: 10.1145/3394171.3413925 http://dx.doi.org/10.1145/3394171.3413925 ]

Ma K D, Zeng K and Wang Z. 2015. Perceptual quality assessment for multi-exposure image fusion. IEEE Transactions on Image Processing, 24(11): 3345-3356 [DOI: 10.1109/TIP.2015.2442920]

Ma L, Lin J, Shang J J, Zhong W, Fan X, Luo Z X and Liu R S. 2020. Learning multi-scale retinex with residual network for low-light image enhancement//Proceedings of the 3rd Chinese Conference on Pattern Recognition and Computer Vision. Nanjing, China: Springer [ DOI: 10.1007/978-3-030-60633-6_24 http://dx.doi.org/10.1007/978-3-030-60633-6_24 ]

Ma L, Liu R S, Zhang J A, Fan X and Luo Z X. 2021. Learning deep context-sensitive decomposition for low-light image enhancement. IEEE Transactions on Neural Networks and Learning Systems, Early Access [DOI: 10.1109/TNNLS.2021.3071245]

Mittal A, Soundararajan R and Bovik A C. 2013. Making a "completely blind" image quality analyzer. IEEE Signal Processing Letters, 20(3): 209-212 [DOI: 10.1109/LSP.2012.2227726]

Pang T Y, Zheng H, Quan Y H and Ji H. 2021. Recorrupted-to-recorrupted: unsupervised deep learning for image denoising//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 2043-2052 [ DOI: 10.1109/CVPR46437.2021.00208 http://dx.doi.org/10.1109/CVPR46437.2021.00208 ]

Pisano E D, Zong S Q, Hemminger B M, Deluca M, Johnston R E, Muller K, Braeuning M P and Pizer S M. 1998. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 11(4): 193-200 [DOI: 10.1007/BF03178082]

Pizer S M, Amburn E P, Austin J D, Cromartie R, Geselowitz A, Greer T, Ter Haar romeny B, Zimmerman J B and Zuiderveld K. 1987. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3): 355-368 [DOI: 10.1016/s0734-189x(87)80186-x]

Ren W Q, Liu S F, Ma L, Xu Q Q, Xu X Y, Cao X C, Du J P and Yang M H. 2019b. Low-light image enhancement via a deep hybrid network. IEEE Transactions on Image Processing, 28(9): 4364-4375 [DOI: 10.1109/TIP.2019.2910412]

Ren X T, Yang W H, Cheng W H and Liu J Y. 2020. LR3M: robust low-light enhancement via low-rank regularized retinex model. IEEE Transactions on Image Processing, 29: 5862-5876 [DOI: 10.1109/TIP.2020.2984098]

Ren Y R, Ying Z Q, Li T H and Li G. 2019a. LECARM: low-light image enhancement using the camera response model. IEEE Transactions on Circuits and Systems for Video Technology, 29(4): 968-981 [DOI: 10.1109/TCSVT.2018.2828141]

Sakaridis C, Dai D X and van Gool L. 2019. Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 7373-7382 [ DOI: 10.1109/ICCV.2019.00747 http://dx.doi.org/10.1109/ICCV.2019.00747 ]

Sakaridis C, Dai D X and van Gool L. 2020. Map-guided curriculum domain adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6): 3139-3153 [DOI: 10.1109/TPAMI.2020.3045882]

Sakaridis C, Dai D X and van Gool L. 2021. ACDC: the adverse conditions dataset with correspondences for semantic driving scene understanding//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. [s. l.]: IEEE: 10765-10775

Shen L, Yue Z H, Feng F, Chen Q, Liu S H and Ma J. 2017. MSR-Net: low-light image enhancement using deep convolutional network[EB/OL]. [2021-09-17] . https://arxiv.org/pdf/1711.02488.pdf https://arxiv.org/pdf/1711.02488.pdf

Shi Y M, Wu X P and Zhu M. 2019. Low-light image enhancement algorithm based on retinex and generative adversarial network [EB/OL]. [2021-09-17] . https://arxiv.org/pdf/1906.06027.pdf https://arxiv.org/pdf/1906.06027.pdf

Tan X, Zhang Y H, Cao Y, Ma L Z and Lau W H. 2020. Night-time semantic segmentation with a large real dataset [EB/OL] . [2021-09-17]. https://arxiv.org/pdf/2003.06883.pdf https://arxiv.org/pdf/2003.06883.pdf

Tao L, Zhu C, Xiang G Q, Li Y, Jia H Z and Xie X D. 2017. LLCNN: a convolutional neural network for low-light image enhancement//Proceedings of 2017 IEEE Visual Communication and Image Processing. St. Petersburg, USA: IEEE: 1-4 [ DOI: 10.1109/VCIP.2017.8305143 http://dx.doi.org/10.1109/VCIP.2017.8305143 ]

Triantafyllidou D, Moran S, McDonagh S, Parisot S and Slabaugh G. 2020. Low light video enhancement using synthetic data produced with an intermediate domain mapping//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK: Springer: 103-119 [ DOI: 10.1007/978-3-030-58601-0_7 http://dx.doi.org/10.1007/978-3-030-58601-0_7 ]

Wang L W, Liu Z S, Siu W C and Lun D P K. 2020. Lightening network for low-light image enhancement. IEEE Transactions on Image Processing, 29: 7984-7996 [DOI: 10.1109/TIP.2020.3008396]

Wang R X, Xu X G, Fu C W, Lu J B, Yu B and Jia J Y. 2021a. Seeing dynamic scene in the dark: a high-quality video dataset with mechatronic alignment//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. [s. l.]: IEEE

Wang R X, Zhang Q, Fu C W, Shen X Y, Zheng W S and Jia J Y. 2019a. Underexposed photo enhancement using deep illumination estimation//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 6842-6850 [ DOI: 10.1109/CVPR.2019.00701 http://dx.doi.org/10.1109/CVPR.2019.00701 ]

Wang S H, Zheng J, Hu H M and Li B. 2013. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9): 3538-3548 [DOI: 10.1109/TIP.2013.2261309]

Wang W J, Yang W H and Liu J Y. 2021b. HLA-Face: joint high-low adaptation for low light face detection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 16190-16199 [ DOI: 10.1109/CVPR46437.2021.01593 http://dx.doi.org/10.1109/CVPR46437.2021.01593 ]

Wang Y, Cao Y, Zha Z J, Zhang J, Xiong Z W, Zhang W and Wu F. 2019b. Progressive retinex: mutually reinforced illumination-noise perception network for low-light image enhancement//Proceedings of the 27th ACM International Conference on Multimedia. Nice, France: ACM: 2015-2023 [ DOI: 10.1145/3343031.3350983 http://dx.doi.org/10.1145/3343031.3350983 ]

Wang Y, Chen Q and Zhang B. 1999. Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics, 45(1): 68-75 [DOI: 10.1109/30.754419]

Wu H Y, Qu Y Y, Lin S H, ZhouJ, Qiao R Z, Zhang Z Z, Xie Y and Ma L Z. 2021a. Contrastive learning for compact single image Dehazing//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 10546-10555 [ DOI: 10.1109/CVPR46437.2021.01041 http://dx.doi.org/10.1109/CVPR46437.2021.01041 ]

Wu X Y, Wu Z Y, Guo H, Ju L L and Wang S. 2021b. DANNet: a one-stage domain adaptation network for unsupervised nighttime semantic segmentation//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 15764-15773 [ DOI: 10.1109/CVPR46437.2021.01551 http://dx.doi.org/10.1109/CVPR46437.2021.01551 ]

Xu J, Hou Y K, RenD W, Liu L, Zhu F, Yu M Y, Wang H Q and Shao L. 2020a. STAR: a structure and texture aware retinex model. IEEE Transactions on Image Processing, 29: 5022-5037 [DOI: 10.1109/TIP.2020.2974060]

Xu K, Yang X, Yin B C and Lau R W H. 2020b. Learning to restore low-light images via decomposition-and-enhancement//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 2278-2287 [ DOI: 10.1109/CVPR42600.2020.00235 http://dx.doi.org/10.1109/CVPR42600.2020.00235 ]

Xu L, Yan Q, Xia Y and Jia J Y. 2012. Structure extraction from texture via relative total variation. ACM Transactions on Graphics, 31(6): #139 [DOI: 10.1145/2366145.2366158]

Yang S, Luo P, Loy C C and Tang X O. 2016. WIDER FACE: a face detection benchmark//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 5525-5533 [ DOI: 10.1109/CVPR.2016.596 http://dx.doi.org/10.1109/CVPR.2016.596 ]

Yang W H, Wang S Q, Fang Y M, Wang Y and Liu J Y. 2020. From fidelity to perceptual quality: a semi-supervised approach for low-light image enhancement//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 3060-3069 [ DOI: 10.1109/CVPR42600.2020.00313 http://dx.doi.org/10.1109/CVPR42600.2020.00313 ]

Yang W H, Wang S Q, Fang Y M, Wang Y and Liu J Y. 2021. Band representation-based semi-supervised low-light image enhancement: bridging the gap between signal fidelity and perceptual quality. IEEE Transactions on Image Processing, 30: 3461-3473 [DOI: 10.1109/TIP.2021.3062184]

Yasarla R, Sindagi V A and Patel V M. 2021. Semi-supervised image deraining using Gaussian processes. IEEE Transactions on Image Processing, 30: 6570-6582 [DOI: 10.1109/TIP.2021.3096323]

Ye X Y, Luo X H, Wang P and Chen H Y. 2019. Face recognition with superposed linear sparse representation based on discriminative nonconvex low-rank matrix decomposition. Journal of Image and Graphics, 24(8): 1327-1337

叶学义, 罗宵晗, 王鹏, 陈慧云. 2019. 基于非凸低秩分解判别的叠加线性稀疏人脸识别. 中国图象图形学报, 24(8): 1327-1337) [DOI: 10.11834/jig.180585]

Yu F, Xian W Q, Chen Y Y, Liu F C, Liao M, Madhavan V and Darrell T. 2018a. BDD100K: a diverse driving video database with scalable annotation tooling [EB/OL]. [2021-06-12] . https://arxiv.org/pdf/1805.04687.pdf https://arxiv.org/pdf/1805.04687.pdf

Yu S S, Liu W Y, Zhang Y S, Qu Z, Zhao D L and Zhang B. 2018b. DeepExposure: learning to expose photos with asynchronously reinforced adversarial learning//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada: Curran Associates Inc: 2153-2163

Yuan L and Sun J. 2012. Automatic exposure correction of consumer photographs//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer: 771-785 [ DOI: 10.1007/978-3-642-33765-9_55 http://dx.doi.org/10.1007/978-3-642-33765-9_55 ]

Zhai P B, Yang H, Song T T, Yu K, Ma L X and Huang X S. 2020. Two-path semantic segmentation algorithm combining attention mechanism. Journal of Image and Graphics, 25(8): 1627-1636

翟鹏博, 杨浩, 宋婷婷, 余亢, 马龙祥, 黄向生. 2020. 结合注意力机制的双路径语义分割. 中国图象图形学报, 25(8): 1627-1636)[DOI: 10.11834/jig.190533]

Zhang F, Li Y, You S D and Fu Y. 2021c. Learning temporal consistency for low light video enhancement from single images//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 4965-4974 [ DOI: 10.1109/CVPR46437.2021.00493 http://dx.doi.org/10.1109/CVPR46437.2021.00493 ]

Zhang J A, Liu R S, Ma L, Zhong W, Fan X and Luo Z X. 2020c. Principle-inspired multi-scale aggregation network for extremely low-light image enhancement//Proceedings of 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. Barcelona, Spain: IEEE: 2638-2642 [ DOI: 10.1109/ICASSP40776.2020.9053261 http://dx.doi.org/10.1109/ICASSP40776.2020.9053261 ]

Zhang Q, Nie Y W, Zhu L, Xiao C X and Zheng W S. 2020a. Enhancing underexposed photos using perceptually bidirectional similarity. IEEE Transactions on Multimedia, 23: 189-202 [DOI: 10.1109/TMM.2020.2982045]

Zhang Q, Yuan G Z, Xiao C X, Zhu L and Zheng W S. 2018. High-quality exposure correction of underexposed photos//Proceedings of the 26th ACM International Conference on Multimedia. Seoul, Korea(South): ACM: 582-590 [ DOI: 10.1145/3240508.3240595 http://dx.doi.org/10.1145/3240508.3240595 ]

Zhang Y H, Guo X J, Ma J Y, Liu W and Zhang J W. 2021. Beyond brightening low-light images. International Journal of Computer Vision, 129(4): 1013-1037 [DOI: 10.1007/s11263-020-01407-x]

Zhang Y H, Zhang J W and Guo X J. 2019. Kindling the darkness: a practical low-light image enhancer//Proceedings of the 27th ACM International Conference on Multimedia. Nice, France: ACM: 1632-1640 [ DOI: 10.1145/3343031.3350926 http://dx.doi.org/10.1145/3343031.3350926 ]

Zhang Y, Di X G, Zhang B and Wang C H. 2020b. Self-supervised image enhancement network: training with low light images only[EB/OL]. [2021-06-17] . https://arxiv.org/pdf/2002.11300.pdf https://arxiv.org/pdf/2002.11300.pdf

Zhao L, Lu S P, Chen T, Yang Z L and Shamir A. 2021. Deep symmetric network for underexposed image enhancement with recurrent attentional learning//Proceedings of 2021 IEEE International Conference on Computer Vision. [s. l.]: IEEE

Zhu G J, Ma L, Liu R S, Fan X and Luo Z X. 2021. Collaborative reflectance-and-illumination learning for high-efficient low-light image enhancement//Proceedings of 2021 IEEE International Conference on Multimedia and Expo. Shenzhen, China: IEEE: 1-6 [ DOI: 10.1109/ICME51207.2021.9428268 http://dx.doi.org/10.1109/ICME51207.2021.9428268 ]

Zhu M F, Pan P B, Chen W and Yang Y. 2020. EEMEFN: low-light image enhancement via edge-enhanced multi-exposure fusion network//Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AIAA: 13106-13113 [ DOI: 10.1609/aaai.v34i07.7013 http://dx.doi.org/10.1609/aaai.v34i07.7013 ]