[1] ZUO C, QIAN J M, FENG S J et al. Deep learning in optical metrology: a review[J]. Light: Science & Applications, 11, 1-54(2022).

[2] SITU G H. Deep holography[J]. Light: Advanced Manufacturing, 3, 1-23(2022).

[3] LUO Y, ZHAO Y F, Li J X et al. Computational imaging without a computer: seeing through random diffusers at the speed of light[J]. eLight, 2, 1-16(2022).

[4] CHEN Q J, WU T T. Single image deraining based on the concatenation residual network[J]. Chinese Journal of Liquid Crystals and Displays, 36, 605-614(2021).

[5] KANG L W, Lin C W, Fu Y H. Automatic single-image-based rain streaks removal via image decomposition[J]. IEEE Transactions on Image Processing, 21, 1742-1755(2011).

[6] LUO Y, XU Y, JI H. Removing rain from a single image via discriminative sparse coding[C], 3397-3405(2015).

[7] LI Y, TAN R T, GUO X J et al. Rain streak removal using layer priors[C], 2736-2744(2016).

[8] YANG W H, TAN R T, FENG J S et al. Deep joint rain detection and removal from a single image[C], 1357-1366(2017).

[9] LI X, WU J L, LIN Z C et al. Recurrent squeeze-and-excitation context aggregation net for single image deraining[C], 254-269(2018).

[10] REN D W, ZUO W M, HU Q H et al. Progressive image deraining networks: A better and simpler baseline[C], 3937-3946(2019).

[11] WANG C, XING X Y, WU Y T et al. Dcsfn: Deep cross-scale fusion network for single image rain removal[C], 1643-1651(2020).

[12] ZHANG H, SINDAGI V, PATEL V M. Image de-raining using a conditional generative adversarial network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 30, 3943-3956(2019).

[13] WEI W, MENG D Y, ZHAO Q et al. Semi-supervised transfer learning for image rain removal[C], 3877-3886(2019).

[14] JIN X, CHEN Z B, LIN J X et al. Unsupervised single image deraining with self-supervised constraints[C], 2761-2765(2019).

[15] LIU J Y, YANG W H, YANG S et al. Erase or fill？deep joint recurrent rain removal and reconstruction in videos[C], 3233-3242(2018).

[16] HU X W, Fu C W, ZHU L et al. Depth-attentional features for single-image rain removal[C], 8022-8031(2019).

[17] HUANG G, LIU Z, Van Der Maaten L et al. Densely connected convolutional networks[C], 4700-4708(2017).

[18] HUANG G, CHEN D L, LI T H et al. Multi-scale dense networks for resource efficient image classification[J]. arXiv(2017).

[19] SUN K, XIAO B, LIU D et al. Deep high-resolution representation learning for human pose estimation[C], 5693-5703(2019).

[20] YU F, WANG D, SHELHAMER E et al. Deep layer aggregation[C], 2403-2412(2018).

[21] LIN M, CHEN Q, YAN S C. Network in network[J/OL]. arXiv(2013).

[22] GAO S H, CHENG M M, ZHAO K et al. Res2net: A new multi-scale backbone architecture[J]. IEEE Transactions on pattern analysis and machine intelligence, 43, 652-662(2019).

[23] CHEN Y P, FAN H Q, XU B et al. Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution[C], 3435-3444(2019).

[24] TAN M X, LE Q V. Mixconv: Mixed depthwise convolutional kernels[J/OL]. arXiv(2019).

[25] DUTA I C, LIU L, ZHU F et al. Pyramidal convolution: Rethinking convolutional neural networks for visual recognition[J/OL]. arXiv(2020).

[26] SZEGEDY C, LIU W, JIA Y Q et al. Going deeper with convolutions[C], 1-9(2015).

[27] LI Y, KUANG Z H, CHEN Y M et al. Data-driven neuron allocation for scale aggregation networks[C], 11526-11534(2019).

[28] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 9, 1735-1780(1997).

[29] ZHOU W, 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).

[30] WANG F, WEI X, GUO J L et al. Multi-scale single image rain removal based on multi-channel separation and integration[J]. Computer and Modernization, 72(2021).

[31] FU X Y, HUANG J B, ZENG D L et al. Removing rain from single images via a deep detail network[C], 3855-3863(2017).

[32] CUI X, SHANG W, Ren D W et al. Semi-supervised single image deraining with discrete wavelet transform[C], 265-278(2021).

[33] MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 20, 209-212(2012).