[1] Chen Y, Zhang J L, Liu H L et al. Self-supervised enhancement of low-light images based on blind spot networks[J]. Acta Optica Sinica, 44, 1310001(2024).

[2] Zhao Y, Miao J L, Liu X F et al. Fatigue driving detection under low illumination based on image enhancement and face state recognition[J]. Laser & Optoelectronics Progress, 61, 2215005(2024).

[3] Liu H M, Jin F, Zeng H et al. Image enhancement guided object detection in visually degraded scenes[J/OL]. IEEE Transactions on Neural Networks and Learning Systems, 1-14. https://ieeexplore.ieee.org/document/10130799

[4] Guo D Y, Liu K Y, Zhang H Y et al. Inverse SNR and complex-valued decorrelation OCTA real-time imaging based on GPU high-speed parallel computing[J]. Chinese Journal of Lasers, 51, 0907011(2024).

[5] Lü F, Cui X Y, Liu T. Multiscale low-light image enhancement algorithm with brightness equalization and edge enhancement algorithm[J]. Laser & Optoelectronics Progress, 61, 1237010(2024).

[6] Xu Q, Jiang H L, Scopigno R et al. A novel approach for enhancing very dark image sequences[J]. Signal Processing, 103, 309-330(2014).

[7] Kong N S P, Ibrahim H, Hoo S C. A literature review on histogram equalization and its variations for digital image enhancement[J]. International Journal of Innovation, 4, 386(2013).

[8] Land E H, McCann J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 61, 1-11(1971).

[9] Wang M L, Tian Z J, Gui W F et al. Low-light image enhancement based on nonsubsampled shearlet transform[J]. IEEE Access, 8, 63162-63174(2020).

[10] Lore K G, Akintayo A, Sarkar S. LLNet: a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 61, 650-662(2017).

[11] Wei C, Wang W J, Yang W H et al. Deep retinex decomposition for low-light enhancement[EB/OL]. https://arxiv.org/abs/1808.04560v1

[12] Zhang Y H, Zhang J W, Guo X J. Kindling the darkness: a practical low-light image enhancer[C], 1632-1640(2019).

[13] Zhang Y H, Guo X J, Ma J Y et al. Beyond brightening low-light images[J]. International Journal of Computer Vision, 129, 1013-1037(2021).

[14] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention–MICCAI 2015, 9351, 234-241(2015).

[15] Zamir S W, Arora A, Khan S et al. Learning enriched features for real image restoration and enhancement[M]. Computer vision-ECCV 2020, 12370, 492-511(2020).

[16] Chen Y S, Wang Y C, Kao M H et al. Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs[C], 6306-6314(2018).

[17] Guo C L, Li C Y, Guo J C et al. Zero-reference deep curve estimation for low-light image enhancement[C], 1777-1786(2020).

[18] Jiang Y F, Gong X Y, Liu D et al. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 30, 2340-2349(2021).

[19] Shi W L, Liao Y P, Xu Z M et al. A low-light image enhancement method combined with generative adversarial networks in nonsubsampled shearlet transform domain[J]. Laser & Optoelectronics Progress, 60, 2410007(2023).

[20] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

[21] Dang J C, Zhong Y, Qin X L. PPformer: using pixel-wise and patch-wise cross-attention for low-light image enhancement[J]. Computer Vision and Image Understanding, 241, 103930(2024).

[22] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[23] Wang T, Zhang K H, Shen T R et al. Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 2654-2662(2023).

[24] Wang R X, Zhang Q, Fu C W et al. Underexposed photo enhancement using deep illumination estimation[C], 6842-6850(2019).

[25] Kingma D P, Ba J, Hammad M M. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980v9

[26] Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts[EB/OL]. https://arxiv.org/abs/1608.03983v5

[27] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).

[28] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[29] Zhang R, Isola P, Efros A A et al. The unreasonable effectiveness of deep features as a perceptual metric[C], 586-595(2018).

[30] Wang L W, Liu Z S, Siu W C et al. Lightening network for low-light image enhancement[J]. IEEE Transactions on Image Processing, 29, 7984-7996(2020).

[31] Wang Z D, Cun X D, Bao J M et al. Uformer: a general U-shaped transformer for image restoration[C], 17662-17672(2022).

[32] Zamir S W, Arora A, Khan S et al. Restormer: efficient transformer for high-resolution image restoration[C], 5718-5729(2022).

[33] Lim S, Kim W. DSLR: deep stacked Laplacian restorer for low-light image enhancement[J]. IEEE Transactions on Multimedia, 23, 4272-4284(2021).