[1] [1] Guo X, Hu Q. Low-light image enhancement via breaking down the darkness[J]. International Journal of Computer Vision, 2023, 131(1): 48-66.

[2] [2] Zamir S W, Arora A, Khan S, et al. Learning enriched features for real image restoration and enhancement[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXV 16. Springer International Publishing, 2020: 492-511.

[3] [3] Guo M H, Xu T X, Liu J J, et al. Attention mechanisms in computer vision: A survey[J]. Computational visual media, 2022, 8(3): 331-368.

[4] [4] Xu X, Wang R, Fu C W, et al. SNR-aware low-light image enhancement[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 17714-17724.

[5] [5] Tu Z, Talebi H, Zhang H, et al. Maxim: Multi-axis mlp for image processing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 5769-5780.

[6] [6] Fan C M, Liu T J, Liu K H. Half wavelet attention on MNet+ for low-light image enhancement[C]//2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022: 3878-3882.

[7] [7] Tian C, Xu Y, Zuo W, et al. Asymmetric CNN for image superresolution[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(6): 3718-3730.

[8] [8] Tai Y, Yang J, Liu X, et al. Memnet: A persistent memory network for image restoration[C]//Proceedings of the IEEE international conference on computer vision. 2017: 4539-4547.

[9] [9] Dai Y, Gieseke F, Oehmcke S, et al. Attentional feature fusion[C]//Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2021: 3560-3569.

[10] [10] Wang Z, Cun X, Bao J, et al. Uformer: A general ushaped transformer for image restoration[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 17683-17693.

[11] [11] Ma L, Liu R, Zhang J, et al. Learning deep context-sensitive decomposition for low-light image enhancement[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(10): 5666-5680.

[12] [12] Wang Y, Liu Z, Liu J, et al. Low-light image enhancement with illumination-aware gamma correction and complete image modelling network[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 13128-13137.

[13] [13] Ma L, Liu R, Zhang J, et al. Learning deep context-sensitive decomposition for low-light image enhancement[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(10): 5666-5680.

[14] [14] Lu Y, Huang B. Structured output learning with conditional generative flows[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(04): 5005-5012.

[15] [15] Wang Y, Wan R, Yang W, et al. Low-light image enhancement with normalizing flow[C]//Proceedings of the AAAI conference on artificial intelligence. 2022, 36(3): 2604-2612.

[16] [16] Sun Y, Chen J, Liu Q, et al. Dual-path attention network for compressed sensing image reconstruction[J]. IEEE Transactions on Image Processing, 2020, 29: 9482-9495.

[17] [17] Tabernik D, Kristan M, Leonardis A. Spatially-adaptive filter units for compact and efficient deep neural networks[J]. International Journal of Computer Vision, 2020, 128(8-9): 2049-2067.

[18] [18] Tabernik D, Kristan M, Leonardis A. Spatially-adaptive filter units for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 9388-9396.

[19] [19] Keller T A, Peters J W T, Jaini P, et al. Self normalizing flows[C]//International Conference on Machine Learning. PMLR, 2021: 5378-5387.

[20] [20] Loh Y P, Liang X, Chan C S. Low-light image enhancement using Gaussian Process for features retrieval[J]. Signal Processing: Image Communication, 2019, 74: 175-190.

[21] [21] Hoogeboom E, Van Den Berg R, Welling M. Emergingconvolutions for generative normalizing flows[C]//International conference on machine learning. PMLR, 2019: 2771-2780.

[22] [22] Zhang D, Zhang H, Tang J, et al. Feature pyramid transformer[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVIII 16. Springer International Publishing, 2020: 323-339.

[23] [23] Yang W, Wang W, Huang H, et al. Sparse gradient regularized deep retinex network for robust low-light image enhancement[J]. IEEE Transactions on Image Processing, 2021, 30: 2072-2086.

[24] [24] Zhang Z, Zheng H, Hong R, et al. Deep color consistent network for low-light image enhancement[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 1899-1908.

[25] [25] Yang W, Wang S, Fang Y, et al. Band representation-based semi - supervised low - light image enhancement: Bridging the gap between signal fidelity and perceptual quality[J]. IEEE Transactions on Image Processing, 2021, 30: 3461-3473.