[1] [1] IBRAHIM H, KONG N S P. Brightness preserving dynamic histogram equalization for image contrast enhancement[J]. IEEE transactions on consumer electronics, 2007, 53(4): 1752-1758.

[2] [2] CHENG H D. A simple and effective histogram equalization approach to image enhancement[J]. Digital signal processing, 2004, 14(2): 158-170.

[3] [3] LAND E H. The Retinex theory of color vision[J]. Scientific American, 1977, 237(6): 108-129.

[4] [4] JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale Retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE transactions on image processing, 1997, 6(7): 965-976.

[5] [5] GUO X J, LI Y, LING H B. LIME: low-light image enhancement via illumination map estimation[J]. IEEE transactions on image processing, 2016, 26(2): 982-993.

[6] [6] CHEN W, WANG W J, YANG W H. Deep Retinex decomposition for low-light enhancement[EB/OL]. (2018-08-14) [2023-12-26]. https://arxiv.org/abs/1808.04560.

[7] [7] ZHANG Y H, ZHANG J W, GUO X J. Kindling the darkness: a practical low-light image enhancer[C]//Proceedings of 2019 ACM International Conference on Multimedia, October 21-25, 2019, Nice, France. New York: ACM, 2019: 1632-1640.

[8] [8] HAI J, XUAN Z, YANG R. R2RNet: low-light image enhancement via real-low to real-normal network[J]. Journal of visual communication and image representation, 2023, 90: 103712.

[9] [9] MA Q, WANG Y, ZENG T Y. Retinex-based variational framework for low-light image enhancement and denoising[J]. IEEE transactions on multimedia, 2022, 25: 5580-5588.

[10] [10] MA L, MA T Y, LIU R S. Toward fast, flexible, and robust low-light image enhancement[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 19-23, 2022, New Orleans, USA. New York: IEEE, 2022: 5637-5646.

[11] [11] WU W H, WENG J, ZHANG P P. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement[C]//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 19-23, 2022, New Orleans, USA. New York: IEEE, 2022: 5901-5910.

[12] [12] YANG S Z, WU Y M, LI Z H. Implicit neural representation for cooperative low-light image enhancement[C]//Proceedings of 2023 IEEE/CVF Conference on Computer Vision, October 1-6, 2023, Paris, France. New York: IEEE, 2023: 12918-12927.

[13] [13] FEI B, LYU Z Y, PAN L, et al. Generative diffusion prior for unified image restoration and enhancement[C]//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-22, 2023, Vancouver, Canada. New York: IEEE, 2023: 9935-9946.

[14] [14] YANG L X, ZHANG R Y, LI L D, et al. SimAm: a simple, parameter-free attention module for convolutional neural networks[C]//Proceedings of 2021 International Conference on Machine Learning, July 18-24, 2021, online. New York: IEEE, 2021: 11863-11874.

[15] [15] HE K M, ZHANG X Y, REN S Q. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2-July 1, 2016, Las Vegas, USA. New York: IEEE, 2016: 770-778.

[16] [16] WANG W J, WEI C, YANG W H, et al. GLADNet: low-light enhancement network with global awareness[C]//Proceeding of the 2018 IEEE International Conference on Automatic Face & Gesture Recognition, May 15-19, 2018, Xiamen, China. New York: IEEE, 2018: 751-755.

[17] [17] ZHANG Y H, GUO X J, MA J Y, et al. Beyond brightening low-light images[J]. International journal of computer vision, 2021, 129: 1013-1037.

[18] [18] LI C Y, GUO C L, CHEN C L, et al. Learning to enhance low-light image via zero-reference deep curve estimation[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(8): 4225-4238.

[19] [19] JIANG Y F, GONG X Y, LIU D. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE transactions on image processing, 2021, 30: 2340-2349.

[20] [20] WANG S H, ZHENG J, HU H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE transactions on image processing, 2013, 22(9): 3538-3548.

[21] [21] MA K D, ZENG K, WANG Z, et al. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE transactions on image processing, 2015, 24(11): 3345-3356.

[22] [22] YANG W H, WANG S Q, FANG Y M, et al. From fidelity to perceptual quality: a semi-supervised approach for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, USA. New York: IEEE, 2020: 3063-3072.

[23] [23] GUO C L, LI C Y, GUO J C, et al. Zero-reference deep curve estimation for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, USA. New York: IEEE, 2020: 1780-1789.

[24] [24] ZHU X H, SU W J, LU L W, et al. Deformable DETR: deformable transformers for end-to-end object detection[C]//Proceedings of 2021 International Conference on Learning Representation, May 3-7, 2021, online.2020: 11-16.

[25] [25] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding yolo series in 2021[EB/OL]. (2021-07-18) [2023-12-26]. https://arxiv.org/abs/2107.08430.

[26] [26] LOH Y P, CHAN C S. Getting to know low-light images with the exclusively dark dataset[J]. Computer vision and image understanding, 2019, 178: 30-42.