[1] [1] 1孙家抦， 舒宁， 关泽群. 遥感原理、方法和应用［M］. 北京： 测绘出版社， 1997.SUNJ B， SHUN， GUANZ Q. Principle， Method and Application of Remote Sensing［M］. Beijing： Sino Maps Press， 1997.（in Chinese）

[2] [2] 2柏宇阳. 基于卷积神经网络的遥感图像超分辨重建［D］. 哈尔滨： 黑龙江大学， 2021.BAIY Y. Super-Resolution Reconstruction of Remote Sensing Image Based on Convolutional Neural Network［D］. Harbin： Helongjiang University， 2021. （in Chinese）

[3] [3] 3唐艳秋， 潘泓， 朱亚平， 等. 图像超分辨率重建研究综述［J］. 电子学报， 2020， 48（7）：1407-1420. doi: 10.3969/j.issn.0372-2112.2020.07.022TANGY Q， PANH， ZHUY P， et al. A survey of image super-resolution reconstruction［J］. Acta Electronica Sinica， 2020， 48（7）：1407-1420.（in Chinese）. doi: 10.3969/j.issn.0372-2112.2020.07.022

[4] [4] 4熊鹰飞. 基于生成对抗网络的多源跨区域遥感图像超分辨［D］. 深圳： 中国科学院大学（中国科学院深圳先进技术研究院）， 2021.XIONGY F. Super-Resolution of Multi-Source and Cross-Regional Remote Sensing Images Based on Generative Countermeasure Network［D］. Shenzhen： Shenzhen Institutes of Advanced Technology， Chinese Academy of Sciences， 2021. （in Chinese）

[5] [5] 5邵旻昊. 基于深度学习的遥感图像超分辨率重建算法研究［D］. 西安： 西安电子科技大学， 2021.SHAOM H. Research on Super-Resolution Reconstruction Algorithm of Remote Sensing Image Based on Deep Learning［D］. Xi'an： Xidian University， 2021. （in Chinese）

[6] DONG C, LOY C C, HE K M et al. Learning a Deep Convolutional Network for Image Super-Resolution[M]. Computer Vision - ECCV 2014, 184-199(2014).

[7] [7] 7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

[8] [8] 8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

[9] [9] 9耿铭昆， 吴凡路， 王栋. 轻量化火星遥感影像超分辨率重建网络［J］. 光学 精密工程， 2022， 30（12）：1487-1498. doi: 10.37188/OPE.20223012.1487GENGM K， WUF L， WANGD. Lightweight Mars remote sensing image super-resolution reconstruction network［J］. Opt. Precision Eng.， 2022， 30（12）：1487-1498.（in Chinese）. doi: 10.37188/OPE.20223012.1487

[10] WANG X T, YU K, WU S X et al. ESRGAN： Enhanced Super-Resolution Generative Adversarial Networks[M]. Lecture Notes in Computer Science, 63-79(2019).

[11] JI X Z, CAO Y, TAI Y et al. Real-World Super-Resolution via Kernel Estimation and Noise Injection[C], 1914-1923(14).

[12] XU Y Y, LUO W, HU A N et al. TE-SAGAN： an improved generative adversarial network for remote sensing super-resolution images[J]. Remote Sensing, 14, 2425(2022).

[13] WANG X T, XIE L B, DONG C et al. Real-ESRGAN： Training Real-World Blind Super-Resolution with Pure Synthetic Data[C], 1905-1914(11).

[14] ZHANG K, LIANG J Y, VAN GOOL L et al. Designing a Practical Degradation Model for Deep Blind Image Super-Resolution[C], 4771-4780(10).

[15] LEDIG C, THEIS L, HUSZÁR F et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network[C], 105-114(21).

[16] SCHÖNFELD E, SCHIELE B, KHOREVA A. A U-Net Based Discriminator for Generative Adversarial Networks[C], 8204-8213(13).

[17] XU L, REN J S J, YAN Q et al. Deep edge-aware filters[J]. 32nd International Conference on Machine Learning, 2, 1669-1678(2015).

[18] LIU S F, PAN J S, YANG M H. Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network[M]. Computer Vision - ECCV 2016, 560-576(2016).

[19] KAMGAR-PARSI B, KAMGAR-PARSI B, ROSENFELD A. Optimally isotropic Laplacian operator[J]. IEEE Transactions on Image Processing, 8, 1467-1472(1999).

[20] LAI W S, HUANG J B, AHUJA N et al. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution[C], 5835-5843(21).

[21] KIM H et al. Enhanced Deep Residual Networks for Single Image Super-Resolution[C], 1132-1140(21).

[22] BUADES A, COLL B, MOREL J M. A Non-Local Algorithm for Image Denoising[C], 60-65(20).

[23] DABOV K, KATKOVNIK V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 16, 2080-2095(2007).

[24] WANG X L, GIRSHICK R, GUPTA A et al. Non-Local Neural Networks[C], 7794-7803(18).

[25] DAI T, CAI J R, ZHANG Y B et al. Second-Order Attention Network for Single Image Super-Resolution[C], 11057-11066(15).

[26] 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： a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).

[27] YUAN F, HUANG L F, YAO Y. An Improved PSNR Algorithm for Objective Video Quality Evaluation[C], 376-380(26).