[1] HARRIS J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 54, 931(1964).

[2] TSAI R, HUANG T S. Multiple frame image restoration and registration[J]. Advances in Computer Vision and Image Processing, 1, 317-319(1984).

[3] KIM K I, KWON Y. Single-image super-resolution using sparse regression and natural image prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1127-1133(2010).

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

[5] [5] 5彭真明， 景亮， 何艳敏， 等. 基于多尺度稀疏字典的多聚焦图像超分辨融合［J］. 光学 精密工程， 2014， 22（1）： 169-176. doi: 10.3788/OPE.20142201.0169PENGZ M， JINGL， HEY M， et al. Superresolution fusion of multi-focus image based on multiscale sparse dictionary［J］. Optics and Precision Engineering， 2014， 22（1）： 169-176.（in Chinese）. doi: 10.3788/OPE.20142201.0169

[6] ZHANG K B, GAO X B, TAO D C et al. Single image super-resolution with non-local means and steering kernel regression[J]. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 21, 4544-4556(2012).

[7] HE H, SIU W C. Single image super-resolution using Gaussian process regression[C], 449-456(2011).

[8] [8] 8李勇， 王珂， 张立保， 等. 多断层融合的肺CT肿瘤靶区超分辨率重建［J］. 光学 精密工程， 2010， 18（5）： 1212-1218. doi: 10.3788/OPE.20101805.1212LIY， WANGK， ZHANGL B， et al. Super-resolution reconstruction of pulmonary nodules based on CT multi-section fusion［J］. Optics and Precision Engineering， 2010， 18（5）： 1212-1218.（in Chinese）. doi: 10.3788/OPE.20101805.1212

[9] [9] 9吴炜， 杨晓敏， 陈默， 等. 一种新颖的人脸图像超分辨率技术［J］. 光学 精密工程， 2008， 16（5）： 815-821. doi: 10.3321/j.issn:1004-924X.2008.05.010WUW， YANGX M， CHENM， et al. Novel method of face hallucination［J］. Optics and Precision Engineering， 2008， 16（5）： 815-821.（in Chinese）. doi: 10.3321/j.issn:1004-924X.2008.05.010

[10] [10] 10符冉迪， 周颖， 颜文， 等. 基于TV-L1分解的红外云图超分辨率算法［J］. 光学 精密工程， 2016， 24（4）： 937-944. doi: 10.3788/OPE.20162404.0937FUR D， ZHOUY， YANW， et al. Infrared nephogram super-resolution algorithm based on TV-L1 decomposition［J］. Optics and Precision Engineering， 2016， 24（4）： 937-944.（in Chinese）. doi: 10.3788/OPE.20162404.0937

[11] [11] 11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］. Optics and Precision Engineering， 2019， 27（3）： 718-725.（in Chinese）. doi: 10.13482/j.issn1001-7011.2019.05.004

[12] LIU S C, ZHAO H, DU Q et al. Novel cross-resolution feature-level fusion for joint classification of multispectral and panchromatic remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[13] ZHENG Y J, LIU S C, DU Q et al. A novel multitemporal deep fusion network （MDFN） for short-term multitemporal HR images classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10691-10704(14).

[14] 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).

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

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

[17] ADELSON E H, ANDERSON C H, BERGEN J R et al. Pyramid methods in image processing[J]. RCA engineer, 29, 33-41(1984).

[18] BURT P J, ADELSON E H. The Laplacian Pyramid as a Compact Image Code[M]. Readings in Computer Vision, 671-679(1987).

[19] PARIS S, HASINOFF S W, KAUTZ J. Local Laplacian filters： edge-aware image processing with a Laplacian pyramid[J]. Communications of the ACM, 58, 81-91(2015).

[20] GHIASI G, FOWLKES C C. Laplacian pyramid reconstruction and refinement for semantic segmentation[C], 2016, 519-534(2016).

[21] WANG W C, CHANG F L. A multi-focus image fusion method based on Laplacian pyramid[J]. Journal of Computers, 6, 2559-2566(2011).

[22] DENTON E L, CHINTALA S, FERGUS R. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks[J]. Advances in Neural Information Processing System, 28(2015).

[23] LAI W S, HUANG J B, AHUJA N et al. Fast and accurate image super-resolution with deep Laplacian pyramid networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2599-2613(2019).

[24] MCEWEN A S, ELIASON E M, BERGSTROM J W et al. Mars reconnaissance orbiter's high resolution imaging science experiment （HiRISE）[J]. Journal of Geophysical Research： Planets, 112, E05-02(2007).

[25] HE K M, ZHANG X Y, REN S Q et al. Delving deep into rectifiers： surpassing human-level performance on ImageNet classification[C], 1026-1034(2015).