[1] [1] Dong C, Loy C C, He K, et al. Learning a deep convolutional wk f image superresolution [C]European Conference on Computer Vision, 2014: 184199.

[2] [2] Kim J, Lee J K, Lee K M. Deeplyrecursive convolutional wk f image superresolution [C]Proceedings of the IEEE, Conference on Computer Vision Pattern Recognition, 2016: 16371645.

[3] [3] Lim B, Son S, Kim H, et al. Enhanced deep residual wks f single image superresolution [C]Proceedings of the IEEE, Conference on Computer Vision Pattern Recognition Wkshops, 2017: 136144.

[4] [4] Zhang Y, Li K, Li K, et al. Image superresolution using very deep residual channel attention wks [C]Proceedings of the European Conference on Computer Vision (ECCV), 2018: 286301.

[5] Xiu Zhang, Wei Zhou, Zhemin Duan, et al. Convolutional sparse auto-encoder for image super-resolution reconstruction. Infrared and Laser Engineering, 48, 0126005(2019).

[6] Zikang Wei, Yunqing Liu. Gray image super-resolution reconstruction based on improved RDN method. Infrared and Laser Engineering, 49, 20200173(2020).

[7] [7] Efrat N, Glasner D, Apartsin A, et al. Accurate blur models vs image pris in single image superresolution [C]Proceedings of the IEEE, International Conference on Computer Vision (ICCV), 2013: 28322839.

[8] A Liu, Y Liu, J Gu, et al. Blind image super-resolution: A survey and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5461-5480.(2023).

[9] [9] BellKligler S, Shocher A, Irani M. Blind superresolution kernel estimation using an internalgan [C]Advances in Neural Infmation Processing Systems, 2019: 284293.

[10] [10] Liang J, Zhang K, Gu S, et al. Flowbased kernel pri with application to blind superresolution [C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2021: 1060110610.

[11] [11] Gu J, Lu H, Zuo W, et al. Blind superresolution with iterative kernel crection[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2019: 16041613.

[12] [12] Liang J, Sun G, Zhang K, et al. Mutual affine wk f spatially variant kernel estimation in blind image superresolution[C]Proceedings of the IEEECVF International Conference on Computer Vision, 2021: 40964105.

[13] [13] Chen X, Zhang J, Xu C, et al. Better “CMOS” produces clearer images: learning spacevariant blur estimation f blind image superresolution [C]2023 IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2023: 16511661.

[14] [14] Kim S, Sim H Kim M, KOALA: Blind superresolution using kerneliented adaptive local adjustment [C]2021 IEEECVF Conference on Computer Vision Pattern Recognition (CVPR), 2021: 1060610615.

[15] [15] Joshi N, Szeliski R, Kriegman D J. PSF estimation using sharp edge prediction [C]2008 IEEE Conference on Computer Vision Pattern Recognition, 2008: 18.

[16] [16] Kee E, Paris S, Chen S, et al. Modeling removing spatiallyvarying optical blur [C]2011 IEEE International Conference on Computational Photography (ICCP), 2011: 18.

[17] Junfeng Cao, Qinghai Ding, Haibo Luo. Infrared image Super-resolution based on spatially-variant blur kernel calibration. Infrared and Laser Engineering, 53, 20230252(2024).

[18] [18] Olivieri M, Pieri S, Romoli A. Analysis of defocusing thermal effects in optical systems[C]Optical Instrumentation Systems Design, SPIE, 1996, 2774: 81.

[19] [19] Zhang K, Gool L V, Timofte R. Deep unfolding wk f image superresolution[C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2020: 32173226.