[1] Ge X X, Cui H H, Xu Z L et al. Super-resolution image reconstruction method for micro defects of metal engine blades[J]. Acta Optica Sinica, 43, 0210001(2023).

[2] Xu X Y, Deng H P, Xiang S et al. Light field image super-resolution based on feature interaction fusion and attention mechanism[J]. Laser & Optoelectronics Progress, 60, 1410017(2023).

[3] Pan M X, Qu N, Xia Y R et al. Super-resolution reconstruction of nuclear magnetic resonance images based on deep learning[J]. Laser & Optoelectronics Progress, 59, 2217001(2022).

[4] Dong C, Loy C C, He K M, 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).

[5] 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).

[6] Kong F Y, Li M X, Liu S W et al. Residual local feature network for efficient super-resolution[C], 765-775(2022).

[7] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution[C], 2472-2481(2018).

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

[9] Dong X Y, Bao J M, Chen D D et al. CSWin transformer: a general vision transformer backbone with cross-shaped windows[C], 12114-12124(2022).

[10] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2022).

[11] Liang J Y, Cao J Z, Sun G L et al. SwinIR: image restoration using swin transformer[C], 1833-1844(2021).

[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] Niu B, Wen W L, Ren W Q, Bartoli A, Fusiello A et al. Single image super-resolution via a holistic attention network[M]. Computer vision-ECCV 2020 workshops. Lecture notes in computer science, 191-207(2020).

[14] Dai T, Cai J R, Zhang Y B et al. Second-order attention network for single image super-resolution[C], 11057-11066(2020).

[15] Xia B, Hang Y C, Tian Y P et al. Efficient non-local contrastive attention for image super-resolution[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2759-2767(2022).

[16] Peng X J, Xu D, Kong L Y et al. L1-norm loss based twin support vector machine for data recognition[J]. Information Sciences, 340/341, 86-103(2016).

[17] Li X L, Zhang X T, Huang W et al. Truncation cross entropy loss for remote sensing image captioning[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 5246-5257(2021).

[18] Liu Y X, Ge G Y, Qi Z L et al. Research on embroidery image restoration based on improved DCGAN[J]. Laser & Optoelectronics Progress, 60, 2010005(2023).

[19] Rad M S, Bozorgtabar B, Marti U V et al. SROBB: targeted perceptual loss for single image super-resolution[C], 2710-2719(2020).

[20] Zamir S W, Arora A, Khan S et al. Restormer: efficient transformer for high-resolution image restoration[C], 5718-5729(2022).

[21] Ruan H H, Tan Z Y, Chen L T et al. Efficient sub-pixel convolutional neural network for terahertz image super-resolution[J]. Optics Letters, 47, 3115-3118(2022).

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

[23] Bevilacqua M, Roumy A, Guillemot C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 135.1-135.10(2012).

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

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

[26] Arbeláez P, Maire M, Fowlkes C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011).

[27] Cai J R, Zeng H, Yong H W et al. Toward real-world single image super-resolution: a new benchmark and a new model[C], 3086-3095(2020).