[1] Hu F, Lin Y, Hou M D et al. Super-resolution reconstruction of cytoskeleton image based on deep learning[J]. Acta Optica Sinica, 40, 2410001(2020).

[2] Yang J C, Wright J, Huang T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010).

[3] Timofte R, de Smet V, van Gool L. A+: adjusted anchored neighborhood regression for fast super-resolution[M]. Cremers D, Reid I, Saito H, et al. Computer vision-ACCV 2014. Lecture notes in computer science, 9006, 111-126(2015).

[4] Schulter S, Leistner C, Bischof H. Fast and accurate image upscaling with super-resolution forests[C], 3791-3799(2015).

[5] Wang Y T, Zhou H Q, Yan J X et al. Advances in computational optics based on deep learning[J]. Chinese Journal of Lasers, 48, 1918004(2021).

[6] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8692, 184-199(2014).

[7] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9906, 391-407(2016).

[8] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C], 624-632(2017).

[9] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

[10] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

[11] Tai Y, Yang J, Liu X M. Image super-resolution via deep recursive residual network[C], 2790-2798(2017).

[12] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).

[13] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 294-310(2018).

[14] Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11214, 256-272(2018).

[15] Li B, Wang B, Liu J B et al. S-LWSR: super lightweight super-resolution network[J]. IEEE Transactions on Image Processing, 29, 8368-8380(2020).

[16] Tian C W, Zhuge R, Wu Z H et al. Lightweight image super-resolution with enhanced CNN[J]. Knowledge-Based Systems, 205, 106235(2020).

[17] Chu X X, Zhang B, Ma H L et al. Fast, accurate and lightweight super-resolution with neural architecture search[C], 59-64(2019).

[18] Tian C W, Xu Y, Zuo W M et al. Asymmetric CNN for image superresolution[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 3718-3730(2022).

[19] Wang B F, Zhao H T. Small object detection in hyperspectral images based on radial basis activation function[J]. Acta Optica Sinica, 41, 2311001(2021).

[20] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).

[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] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

[23] Li X, Hu X L, Yang J. Spatial group-wise enhance improving semantic feature learning in convolutional networks[EB/OL]. https://arxiv.org/abs/1905.09646

[24] Chen H Y, Gu J J, Zhang Z. Attention in attention network for image super-resolution[EB/OL]. https://arxiv.org/abs/2104.09497

[25] Timofte R, Agustsson E, Gool L V et al. NTIRE 2017 challenge on single image super-resolution: methods and results[C], 1110-1121(2017).

[26] Bevilacqua M, Roumy A, Guillemot C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 135(2012).

[27] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Boissonnat J D, Chenin P, Cohen A, et al. Curves and surfaces. Lecture notes in computer science, 6920, 711-730(2012).

[28] Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C], 416-423(2001).