[1] Li Q, Wang X Y, He J W. Improved algorithm for super-resolution reconstruction of remote-sensing images based on generative adversarial networks[J]. Laser & Optoelectronics Progress, 60, 1028010(2023).

[2] Feng R Y, Qian J Y, Peng Y J et al. Femtosecond infrared optical vortex lasers based on optical parametric amplification[J]. High Power Laser Science and Engineering, 10, e29(2022).

[3] Reimold M, Assenbaum S, Beyreuther E et al. OCTOPOD: single-bunch tomography for angular-spectral characterization of laser-driven protons[J]. High Power Laser Science and Engineering, 11, e68(2023).

[4] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

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

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

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

[8] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[9] Woo S, Park J, Lee J Y, Ferrari V, Hebert M, Sminchisescu C et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[10] Guo M H, Lu C Z, Liu Z N et al. Visual attention network[J]. Computational Visual Media, 9, 733-752(2023).

[11] Ding X H, Zhang X Y, Han J G et al. Scaling up your kernels to 31 × 31: revisiting large kernel design in CNNs[C], 11953-11965(2022).

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

[13] Zhao H Y, Kong X T, He J W, Karlinsky L, Michaeli T, Nishino K et al. Efficient image super-resolution using pixel attention[M]. Computer vision-ECCV 2022 workshops. Lecture notes in computer science, 12537, 56-72(2020).

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

[15] Chen Y P, Dai X Y, Liu M C et al. Dynamic convolution: attention over convolution kernels[C], 11027-11036(2020).

[16] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[17] Ma N N, Zhang X Y, Zheng H T, Ferrari V, Hebert M, Sminchisescu C et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11218, 122-138(2018).

[18] Agustsson E, Timofte R. NTIRE 2017 challenge on single image super-resolution: dataset and study[C], 1122-1131(2017).

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

[20] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Curves and surfaces. Lecture notes in computer science, 6920, 711-730(2012).

[21] 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(2002).

[22] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C], 5197-5206(2015).

[23] Matsui Y, Ito K, Aramaki Y et al. Sketch-based manga retrieval using manga109 dataset[J]. Multimedia Tools and Applications, 76, 21811-21838(2017).

[24] Wang Z, 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).

[25] Hui Z, Gao X B, Yang Y C et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-2032(2019).

[26] Zhang K, Zuo W M, Zhang L. Learning a single convolutional super-resolution network for multiple degradations[C], 3262-3271(2018).

[27] Wang L G, Dong X Y, Wang Y Q et al. Exploring sparsity in image super-resolution for efficient inference[C], 4915-4924(2021).

[28] Qin J H, Zhang R M. Lightweight single image super-resolution with attentive residual refinement network[J]. Neurocomputing, 500, 846-855(2022).

[29] Wu H P, Gui J, Zhang J et al. Pyramidal dense attention networks for single image super-resolution[J]. IET Image Processing, 16, 3247-3257(2022).

[30] Cheng G, Matsune A, Du H et al. Exploring more diverse network architectures for single image super-resolution[J]. Knowledge-Based Systems, 235, 107648(2022).

[31] Tang Y G, Zhang X, Zhang X G. An efficient lightweight network for single image super-resolution[J]. Journal of Visual Communication and Image Representation, 93, 103834(2023).