[1] Kim J H, Lee C, Sim J Y et al. Single-image deraining using an adaptive nonlocal means filter[C], 914-917(2013).

[2] Liu J C, Teng S H, Li Z Y. Removing rain from single image based on details preservation and background enhancement[C], 322-326(2019).

[3] Li Y, Tan R T, Guo X J et al. Rain streak removal using layer priors[C], 2736-2744(2016).

[4] Luo Y, Xu Y, Ji H. Removing rain from a single image via discriminative sparse coding[C], 3397-3405(2015).

[5] Wang K, Li Z W, Zhu C D et al. Local stereo matching algorithm base on secondary guided filtering[J]. Laser & Optoelectronics Progress, 56, 081004(2019).

[6] Fu X Y, Huang J B, Ding X H et al. Clearing the skies: a deep network architecture for single-image rain removal[J]. IEEE Transactions on Image Processing, 26, 2944-2956(2017).

[7] Pan J S, Liu S F, Sun D Q et al. Learning dual convolutional neural networks for low-level vision[C], 3070-3079(2018).

[8] Zhang H, Patel V M. Density-aware single image de-raining using a multi-stream dense network[C], 695-704(2018).

[9] Yang W H, Tan R T, Feng J S et al. Deep joint rain detection and removal from a single image[C], 1685-1694(2017).

[10] Gai S, Wang J S. Image raindrop algorithm research using nonlocal attention enhanced network based on deep learning[J]. Acta Electronica Sinica, 48, 1899-1908(2020).

[11] Wang Y L, Chen C, Zhu S Y et al. A framework of single-image deraining method based on analysis of rain characteristics[C], 4087-4091(2016).

[12] Ding Y, Xue X W, Wang Z Z et al. Domain knowledge driven deep unrolling for rain removal from single image[C], 14-19(2018).

[13] Liu R S, Jiang Z Y, Ma L et al. Deep layer prior optimization for single image rain streaks removal[C], 1408-1412(2018).

[14] Li X, Wu J L, Lin Z C et al. Recurrent squeeze-and-excitation context aggregation net for single image deraining[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 262-277(2018).

[15] Imtiaz M S, Wahid K A. Image enhancement and space-variant color reproduction method for endoscopic images using adaptive sigmoid function[C], 3905-3908(2014).

[16] Kim J K, Park J M, Song K S et al. Adaptive mammographic image enhancement using first derivative and local statistics[J]. IEEE Transactions on Medical Imaging, 16, 495-502(1997).

[17] Ning Y, Li L, Qiu S et al. Underwater range-gated laser imaging system design with video enhancement processing[C], 760-763(2013).

[18] Wang B Y, Zheng F, Jiang J et al. Method for removal of rain and fog in single image[J]. Laser & Optoelectronics Progress, 57, 141027(2020).

[19] Liu Z L, Fu Y. Image dehazing algorithm based on adaptive constraint correction of transmittance[J]. Laser & Optoelectronics Progress, 57, 161003(2020).

[20] Li B Y, Peng X L, Wang Z Y et al. AOD-net: all-in-one dehazing network[C], 4780-4788(2017).

[21] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[22] Basak H, Kundu R, Agarwal A et al. Single image super-resolution using residual channel attention network[C], 219-224(2020).

[23] Li C, Jiang M, Kong J. Multi-branch person re-identification based on multi-scale attention[J]. Laser & Optoelectronics Progress, 57, 201001(2020).

[24] Qiu C C, Zhang S Y, Wang C et al. Improving transfer learning and squeeze-and-excitation networks for small-scale fine-grained fish image classification[J]. IEEE Access, 6, 78503-78512(2018).

[25] Fu J P, Fan W T, Bouguila N. A novel approach for anomaly event detection in videos based on autoencoders and SE networks[C], 179-184(2018).

[26] An G Y, Zhou W, Wu Y X et al. Squeeze-and-excitation on spatial and temporal deep feature space for action recognition[C], 648-653(2018).

[27] Wu J Q, Li Q, Liang S et al. Convolutional neural network with squeeze and excitation modules for image blind deblurring[C], 338-345(2020).

[28] Wang F, Jiang M Q, Qian C et al. Residual attention network for image classification[C], 6450-6458(2017).

[29] Li K P, Wu Z Y, Peng K C et al. Tell me where to look: guided attention inference network[C], 9215-9223(2018).

[30] Huang G, Sun Y, Zhang L et al. Deep networks with stochastic depth[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016, 9908, 646-661(2016).