[1] He G J, Li K L, Hu D Y et al. Information fusion of multisensor satellite remote sensing data :theory, methodology and experiment[J]. Journal of Image and Graphics, 744-750(1999).

[2] Su Y, Ge J J, Wang Y C et al. Research progress on high-resolution imaging system for optical remote sensing in aerospace[J]. Chinese Optics, 16, 258-282(2023).

[3] Zhang W B, Jiang Z. Research on deblurring of remote sensing images based on deep learning[J]. Journal of Yunnan Minzu University (Natural Sciences Edition), 32, 480-484(2023).

[4] Kubala K, Dowski E, Cathey W T. Reducing complexity in computational imaging systems[J]. Optics Express, 11, 2102-2108(2003).

[5] Huang Y, Xiong T. Deep iterative filter adaptive network for simple lens imaging system[J]. Laser & Optoelectronics Progress, 61, 1037003(2024).

[6] Pang Z H, Fan X W, Chen Q F et al. Influence of surface-profile error of larger mirror on aberrations characteristics of optical system[J]. Acta Optica Sinica, 33, 0422002(2013).

[7] Liu S T, Hu R, Li Q H et al. Topology optimization-based lightweight primary mirror design of a large-aperture space telescope[J]. Applied Optics, 53, 8318-8325(2014).

[8] Li W L. Research on image restoration of simple lens imaging[D](2019).

[9] Schuler C J, Hirsch M, Harmeling S et al. Non-stationary correction of optical aberrations[C], 659-666(2011).

[10] Heide F, Rouf M, Hullin M B et al. High-quality computational imaging through simple lenses[J]. ACM Transactions on Graphics (TOG), 32, 149(2013).

[11] Tan Z, Xiangli B, Lü Q B et al. Optics/digital processing co-design method based on aberration optional-correcting[J]. Acta Photonica Sinica, 47, 0511001(2018).

[12] Peng Y F, Sun Q L, Dun X et al. Learned large field-of-view imaging with thin-plate optics[J]. ACM Transactions on Graphics (TOG), 38, 219(2019).

[13] Wu X T, Li J C, Zhou G X et al. RRG-GAN restoring network for simple lens imaging system[J]. Sensors, 21, 3317(2021).

[14] Goodfellow I J, Pouget-Abadie J, Mirza M et al. Generative adversarial nets[C], 2672-2680(2014).

[15] Reed S, Akata Z, Yan X C et al. Generative adversarial text to image synthesis[C], 1681-1690(2016).

[16] Shao Z G, Zhang C S, Chen F X et al. A review on generative adversarial networks for power system applications[J]. Proceedings of the CSEE, 43, 987-1004(2023).

[17] Lu Z J, Shen Y X, Tan Y Q. A WGAN blur insulator image restoration method based on multi-structure fusion[J]. Power System Protection and Control, 52, 166-175(2024).

[18] Kupyn O, Budzan V, Mykhailych M et al. DeblurGAN: blind motion deblurring using conditional adversarial networks[C], 8183-8192(2018).

[19] Kupyn O, Martyniuk T, Wu J R et al. DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better[C], 8877-8886(2019).

[20] Mirza M, Osindero S. Conditional generative adversarial nets[EB/OL]. https:?∥arxiv.org/abs/1411.1784

[21] Hariharan B, Karthic S, Indra Priyadharshini S et al. Hybrid deep convolutional generative adversarial networks (DCGANS) and style generative adversarial network (STYLEGANS) algorithms to improve image quality[C], 1182-1186(2022).

[22] Chen Z N, Zhang H Y, Zeng N Y et al. Attention mechanism embedded multi-scale restoration method for blurred image[J]. Journal of Image and Graphics, 27, 1682-1696(2022).

[23] Wang Y, Lian H B, Wang Y C et al. Super-resolution reconstruction of thermal imaging of power equipment based on improved edge-attentive generative adversarial networks[J]. Power System Protection and Control, 52, 119-127(2024).

[24] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 6000-6010(2017).

[25] Thibos L N, Applegate R A, Schwiegerling J T et al. Standards for reporting the optical aberrations of eyes[J]. Journal of Refractive Surgery, 18, S652-S660(2002).

[26] Geary J M[M]. Introduction to lens design: with practical Zemax examples(2002).

[27] Wei Y Z. Research on joint optics-image design in computational imaging[D](2019).

[28] Liu Z, Lin Y T, Cao Y et al. Swin transformer: hierarchical vision transformer using shifted windows[C], 9992-10002(2021).

[29] Liang J Y, Cao J Z, Sun G L et al. SwinIR: image restoration using swin transformer[C], 1833-1844(2021).

[30] Zamir S W, Arora A, Khan S et al. Restormer: efficient transformer for high-resolution image restoration[C], 5718-5729(2022).

[31] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. Computer Vision-ECCV 2018, 11211, 294-310(2018).

[32] Wang L S, Cao M, Zhong Y et al. Spatial-temporal transformer for video snapshot compressive imaging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 9072-9089(2023).

[33] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks[C], 5967-5976(2017).

[34] Wang Y H. Research on assessment and optimization of remote sensing imaging system based on joint optical-digital optimization[D](2024).

[35] Xia G S, Hu J W, Hu F et al. AID: a benchmark data set for performance evaluation of aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3965-3981(2017).

[36] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https:∥arxiv.org/abs/1412.6980