[1] WU L, LYU G Q, ZHAO C et al. CT image super-resolution reconstruction based on multi-scale residual network[J]. Chinese Journal of Liquid Crystals and Displays, 34, 1006-1012(2019).

[2] LIU G Q, LIU J F, ZHU D H. Image super resolution reconstruction algorithm based on generative countermeasure network[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1720-1727(2021).

[3] LIU Y, ZHU L et al. Review and prospect of image super-resolution technology[J]. Journal of Frontiers of Computer Science and Technology, 14, 181-199(2020).

[4] LI Y, JIN Q Y, ZHAO H C et al. Hyperspectral image reconstruction based on improved residual dense network[J]. Acta Optica Sinica, 41, 0730001(2021).

[5] DONG C, LOY C C, HE K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

[6] SHI W Z, CABALLERO J, HUSZÁR F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C], 1874-1883(2016).

[7] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

[8] LAI W S, HUANG J B, AHUJA N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C], 5835-5843(2017).

[9] ZHANG Y L, LI K P, LI K et al. Image super-resolution using very deep residual channel attention networks[C], 294-310(2018).

[10] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M et al. Generative adversarial nets[C], 2672-2680(2014).

[11] LEDIG C, THEIS L, HUSZÁR F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

[12] WANG X T, YU K, WU S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[C], 63-79(2019).

[13] PENG Y F, ZHANG M T, ZHANG P J et al. Single-image super-resolution reconstruction aggregating residual attention network[J]. Laser & Optoelectronics Progress, 60, 1010017(2023).

[14] ZHANG H, WU C R, ZHANG Z Y et al. ResNeSt: split-attention networks[C], 2735-2745(2022).

[15] PENG Y F, ZHANG P J, GAO Y et al. Attention fusion generative adversarial network for single-image super-resolution reconstruction[J]. Laser & Optoelectronics Progress, 58, 2010012(2021).

[16] MIYATO T, KATAOKA T, KOYAMA M et al. Spectral normalization for generative adversarial networks[C](2018).

[17] JIANG L M, DAI B, WU W et al. Focal frequency loss for image reconstruction and synthesis[C], 13899-13909(2021).

[18] TIMOFTE R, AGUSTSSON E, VAN GOOL L et al. NTIRE 2017 challenge on single image super-resolution: Methods and results[C], 1110-1121(2017).

[19] BEVILACQUA M, ROUMY A, GUILLEMOT C et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C], 1-10(2012).

[20] ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C], 711-730(2010).

[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(2001).

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

[23] YAN Y T, LIU C C, CHEN C Y et al. Fine-grained attention and feature-sharing generative adversarial networks for single image super-resolution[J]. IEEE Transactions on Multimedia, 24, 1473-1487(2022).

[24] AYAZOGLU M. Extremely lightweight quantization robust real-time single-image super resolution for mobile devices[J], 2472-2479(2021).