|
[1]
|
Ibrahim, H. and Sia Pik Kong, N. (2007) Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement. IEEE Transactions on Consumer Electronics, 53, 1752-1758. [Google Scholar] [CrossRef]
|
|
[2]
|
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 Pro-cessing, 29, 5862-5876. [Google Scholar] [CrossRef]
|
|
[3]
|
Gu, Z.H., Li, F., Fang, F.M. and Zhang, G.X. (2020) A Novel Retinex-Based Fractional-Order Variational Model for Images With Severely Low Light. IEEE Transactions on Image Processing, 29, 3239-3253. [Google Scholar] [CrossRef]
|
|
[4]
|
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. [Google Scholar] [CrossRef]
|
|
[5]
|
Li, C.Y., Guo, C.L., Han, L.H., et al. (2022) Low-Light Image and Video Enhancement Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 44, 9396-9416. [Google Scholar] [CrossRef]
|
|
[6]
|
Lv, F.F., Lu, F., Wu, J.H. and Lim, C. (2018) MBLLEN: Low-Light Image/Video Enhancement Using CNNs. BMVC 2018, Newcastle upon Tyne, 3-6 September 2018, 220.
|
|
[7]
|
Zhu, M.F., Pan, P.B., Chen, W. and Yang, Y. (2020) EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network. AAAI 2020, New York, 7-12 February 2020, 13106-13113. [Google Scholar] [CrossRef]
|
|
[8]
|
Wei, C., Wang, W.J., Yang, W.H. and Liu, J.Y. (2018) Deep Retinex Decomposition for Low-Light Enhancement. BMVC 2018, Newcastle upon Tyne, 3-6 September 2018, 155.
|
|
[9]
|
Li, C.Y., Guo, J.C., Porikli, F. and Pang, Y.W. (2018) LightenNet: A Convolutional Neural Network for Weakly Illuminat-ed Image Enhancement. Pattern Recognition Letters, 104, 15-22. [Google Scholar] [CrossRef]
|
|
[10]
|
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 Multime-dia, Nice, 21-25 October 2019, 1632-1640. [Google Scholar] [CrossRef]
|
|
[11]
|
Chen, C., Chen, Q.F., Xu, J. and Koltun, V. (2018) Learning to See in the Dark. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-22 June 2018, 3291-3300. [Google Scholar] [CrossRef]
|
|
[12]
|
李明悦, 晏涛, 井花花, 刘渊. 多尺度特征融合的低照度光场图像增强算法[J/OL]. 计算机科学与探索: 1-14. http://kns.cnki.net/kcms/detail/11.5602.TP.20220507.1144.002.html, 2022-11-29.
|
|
[13]
|
包易峰, 杨德刚. 面向低照度图像增强的注意力曝光融合网络[J/OL]. 计算机工程与应用: 1-10.
http://kns.cnki.net/kcms/detail/11.2127.TP.20221011.1636.010.html, 2022-11-28.
|
|
[14]
|
Jiang, Y.F., Gong, X.Y., Liu, D., et al. (2021) EnlightenGAN: Deep Light Enhancement without Paired Supervision. IEEE Transactions on Image Pro-cessing, 30, 2340-2349. [Google Scholar] [CrossRef]
|
|
[15]
|
吴佳奇, 张文琪, 陈伟, 王帅. 基于改进CycleGAN的煤矿井下低照度图像增强方法[J/OL]. 华中科技大学学报(自然科学版): 1-10. 2022-11-28. [Google Scholar] [CrossRef]
|
|
[16]
|
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. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 3060-3069. [Google Scholar] [CrossRef]
|
|
[17]
|
Guo, C.L., Li, C.Y., Guo, J.C., Loy, C.C., et al. (2020) Ze-ro-Reference Deep Curve Estimation for Low-Light Image Enhancement. 2020 IEEE/CVF Conference on Computer Vi-sion and Pattern Recognition, Seattle, 13-19 June 2020, 1777-1786.
|
|
[18]
|
向森, 王应锋, 邓慧萍, 吴谨, 喻莉. 基于双重迭代的零样本低照度图像增强[J]. 电子与信息学报, 2022, 44(10): 3379-3388.
|
|
[19]
|
Yu, R.S., Liu, W.Y., Zhang, Y.S., et al. (2018) DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning. 32nd International Conference on Neural Information Processing Systems (NeurIPS) 2018, Montreal, 3-8 December 2018, 2153-2163.
|
|
[20]
|
Ma, L., Ma, T.Y., Liu, R.S., Fan, X. and Luo, Z.X. (2022) Toward Fast, Flexible, and Robust Low-Light Image Enhancement. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 18-24 June 2022, 5627-5636.
|