[1] [1] 1朱福珍， 刘越， 黄鑫， 等. 改进的稀疏表示遥感图像超分辨重建［J］. 光学 精密工程， 2019， 27（3）： 718-725. doi: 10.13482/j.issn1001-7011.2019.05.004ZHUF Z， LIUY， HUANGX， et al. Remote sensing image super-resolution based on improved sparse representation［J］. Opt. Precision Eng.， 2019， 27（3）： 718-725.（in Chinese）. doi: 10.13482/j.issn1001-7011.2019.05.004

[2] GUO K H, GUO H F, REN S et al. Towards efficient motion-blurred public security video super-resolution based on back-projection networks[J]. Journal of Network and Computer Applications, 166, 102691(2020).

[3] [3] 3刘雪岩， 许聿达， 雷建昕， 等. 基于视差放大与超分辨率的三维光场腹腔镜标定［J］. 光学 精密工程， 2022， 30（5）： 510-517. doi: 10.37188/OPE.2021.0332LIUX Y， XUY D， LEIJ X， et al. Three-dimensional light field endoscope calibration based on light field disparity amplifier and super-resolution network［J］. Opt. Precision Eng.， 2022， 30（5）： 510-517.（in Chinese）. doi: 10.37188/OPE.2021.0332

[4] LU W, TAN Y P. Color filter array demosaicking： new method and performance measures[J]. IEEE Transactions on Image Processing, 12, 1194-1210(2003).

[5] IRANI M, PELEG S. Improving resolution by image registration[J]. CVGIP： Graphical Models and Image Processing, 53, 231-239(1991).

[6] [6] 6蔡体健， 彭潇雨， 石亚鹏， 等. 通道注意力与残差级联的图像超分辨率重建［J］. 光学 精密工程， 2021， 29（1）： 142-151. doi: 10.37188/OPE.20212901.0142CAIT J， PENGX Y， SHIY P， et al. Channel attention and residual concatenation network for image super-resolution［J］. Opt. Precision Eng.， 2021， 29（1）： 142-151.（in Chinese）. doi: 10.37188/OPE.20212901.0142

[7] [7] 7董本志， 于明聪， 赵鹏. 基于小波域的图像超分辨率重建方法［J］. 液晶与显示， 2021， 36（2）： 317-326. doi: 10.37188/CJLCD.2020-0101DONGB Z， YUM C， ZHAOP. Image super-resolution reconstruction based on wavelet domain［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（2）： 317-326.（in Chinese）. doi: 10.37188/CJLCD.2020-0101

[8] [8] 8陈宗航， 胡海龙， 姚剑敏， 等. 基于改进生成对抗网络的单帧图像超分辨率重建［J］. 液晶与显示， 2021， 36（5）： 705-712. doi: 10.37188/CJLCD.2020-0250CHENZ H， HUH L， YAOJ M， et al. Single frame image super-resolution reconstruction based on improved generative adversarial network［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（5）： 705-712.（in Chinese）. doi: 10.37188/CJLCD.2020-0250

[9] NIU B, WEN W, REN W et al. Single image super-resolution via a holistic attention network[C], 191-207(2020).

[10] 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).

[11] DONG C, LOY C C, TANG X. Accelerating the super-resolution convolutional neural network[C], 391-407(2016).

[12] SHI W, 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).

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

[14] KIM H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2016).

[15] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

[16] TAI Y, YANG J, LIU X M. Image super-resolution via deep recursive residual network[C], 2790-2798(2017).

[17] ZHANG Y L, TIAN Y P, KONG Y et al. Residual dense network for image super-resolution[C], 2472-2481(2018).

[18] LI J C, FANG F M, MEI K F et al. Multi-scale Residual Network for Image Super-resolution[M]. Computer Vision-ECCV 2018, 527-542(2018).

[19] HUI Z, GAO X B, YANG Y C et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-2032(2019).

[20] HE X Y, MO Z T, WANG P S et al. ODE-inspired network design for single image super-resolution[C], 1732-1741(2019).

[21] LI L X, FENG H S, ZHENG B et al. DID： a nested dense in dense structure with variable local dense blocks for super-resolution image reconstruction[C], 2582-2589(2020).

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

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

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

[25] KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics， Speech， and Signal Processing, 29, 1153-1160(1981).

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

[27] LUO X T, XIE Y, ZHANG Y L et al. LatticeNet： towards lightweight image super-resolution with lattice block[C], 2020, 279-289(2020).