[1] DONG X B, XU W Y, MIAO Z H et al. Abandoning the Bayer-filter to see in the dark[C], 17431-17440(2022).

[2] IBRAHIM H, KONG N S P. Brightness preserving dynamic histogram equalization for image contrast enhancement[J]. IEEE Transactions on Consumer Electronics, 53, 1752-1758(2007).

[3] LIANG D, LI L, WEI M Q et al. Semantically contrastive learning for low-light image enhancement[C], 1555-1563(2022).

[4] 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, 22, 3538-3548(2013).

[5] GUO X J, LI Y, LING H B. LIME: low-light image enhancement via illumination map estimation[J]. IEEE Transactions on Image Processing, 26, 982-993(2017).

[6] DONG X, PANG Y, WEN J T. Fast efficient algorithm for enhancement of low lighting video[C], 69(2010).

[7] LI L, WANG R G, WANG W M et al. A low-light image enhancement method for both denoising and contrast enlarging[C], 3730-3734(2015).

[8] 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).

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

[10] GUO C L, LI C Y, GUO J C et al. Zero-reference deep curve estimation for low-light image enhancement[C], 1780-1789(2020).

[11] ZAMIR S W, ARORA A, KHAN S et al. Learning enriched features for fast image restoration and enhancement[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 1934-1948(2023).

[12] ZHOU F, SUN X, DONG J Y et al. SurroundNet: towards effective low-light image enhancement[J]. Pattern Recognition, 141, 109602(2023).

[13] COTOGNI M, CUSANO C. TreEnhance: a tree search method for low-light image enhancement[J]. Pattern Recognition, 136, 109249(2023).

[14] WU W H, WENG J, ZHANG P P et al. URetinex-Net: retinex-based deep unfolding network for low-light image enhancement[C], 5901-5910(2022).

[15] ZHOU L, CAI H M, GU J J et al. Efficient image super-resolution using vast-receptive-field attention[C], 256-272(2023).

[16] ZHANG R. Making convolutional networks shift-invariant again[C], 7324-7334(2019).

[17] ZAMIR S W, ARORA A, KHAN S et al. Restormer: efficient transformer for high-resolution image restoration[C], 5728-5739(2022).

[18] DOSOVITSKIY A, BEYER L, KOLESNIKOV A et al. An image is worth16×16 words: transformers for image recognition at scale[J/OL](2020).

[19] HENDRYCKS D, GIMPEL K. Gaussian error linear units (GELUs)[J/OL](2016).

[20] LI X, WANG W H, HU X L et al. Selective kernel networks[C], 510-519(2019).

[21] JIANG K, WANG Z Y, YI P et al. Multi-scale progressive fusion network for single image deraining[C], 8346-8355(2020).

[22] 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).

[23] WEI C, WANG W J, YANG W H et al. Deep retinex decomposition for low-light enhancement[J/OL](2018).

[24] ZHANG Z, ZHENG H, HONG R C et al. Deep color consistent network for low-light image enhancement[C], 1899-1908(2022).

[25] WANG T, ZHANG K H, SHEN T R et al. Ultra-high-definition low-light image enhancement: a benchmark and transformer-based method[C], 2654-2662(2023).