[1] Yu W K, Zhang X K, Pun M O. Cloud removal in optical remote sensing imagery using multiscale distortion-aware networks[J]. IEEE Geoscience and Remote Sensing Letters, 19, 5512605(2022).

[2] Yu X, Pan J, Wang M et al. A curvature-driven cloud removal method for remote sensing images[EB/OL]. https:∥www.tandfonline.com/doi/full/10.1080/10095020.2023.2189462?scroll=top&needAccess=true

[3] Li X H, Wang L Y, Cheng Q et al. Cloud removal in remote sensing images using nonnegative matrix factorization and error correction[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 103-113(2019).

[4] Xu M, Jia X P, Pickering M et al. Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 149, 215-225(2019).

[5] Zheng K, Li J S, Wang J Q et al. DCLS-GAN: cloud removal method for plateau area of TH-1 satellite image[J]. Acta Geodaetica et Cartographica Sinica, 50, 248-259(2021).

[6] Meng F, Yang X M, Zhou C H et al. A sparse dictionary learning-based adaptive patch inpainting method for thick clouds removal from high-spatial resolution remote sensing imagery[J]. Sensors, 17, 2130(2017).

[7] Meraner A, Ebel P, Zhu X X et al. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion[J]. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 166, 333-346(2020).

[8] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[J]. Communications of the ACM, 63, 139-144(2020).

[9] Singh P, Komodakis N. Cloud-Gan: cloud removal for Sentinel-2 imagery using a cyclic consistent generative adversarial networks[C], 1772-1775(2018).

[10] Pan H. Cloud removal for remote sensing imagery via spatial attention generative adversarial network[EB/OL]. https:∥doi.org/10.48550/arXiv.2009.13015

[11] Zhao Y P, Shen S K, Hu J R et al. Cloud removal using multimodal GAN with adversarial consistency loss[J]. IEEE Geoscience and Remote Sensing Letters, 19, 3093887(2022).

[12] Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models[C], 6840-6851(2020).

[13] Liu J Z, Yuan Z Q, Pan Z Y et al. Diffusion model with detail complement for super-resolution of remote sensing[J]. Remote Sensing, 14, 4834(2022).

[14] Rombach R, Blattmann A, Lorenz D et al. High-resolution image synthesis with latent diffusion models[C], 10684-10695(2022).

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

[16] 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, 13, 600-612(2004).

[17] Wang M Y, Meng X C, Shao F et al. SAR-assisted optical remote sensing image cloud removal method based on deep learning[J]. Acta Optica Sinica, 41, 1228002(2021).

[18] Takagi Y, Nishimoto S. High-resolution image reconstruction with latent diffusion models from human brain activity[C], 14453-14463(2023).

[19] Ronneberger O, Fischer P, Brox T, Navab N, Hornegger J, Wells W M et al. U-net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[20] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https:∥arxiv.org/abs/2010.11929

[21] Jin C, Tong C Q. Remote sensing image classification method based on fusion of CNN and Transformer[J]. Laser & Optoelectronics Progress, 60, 2028006(2023).

[22] He L Y, Bai J Q, He X et al. Sparse transformer based remote sensing rotated object detection[J]. Laser & Optoelectronics Progress, 59, 1810003(2022).

[23] Bao F, Nie S, Xue K W et al. All are worth words: a ViT backbone for diffusion models[C], 22669-22679(2023).

[24] Peebles W, Xie S N. Scalable diffusion models with transformers[C], 4172-4182(2023).

[25] El-Nouby A, Touvron H, Caron M et al. XCiT: cross-covariance image transformers[EB/OL]. http:∥arxiv.org/abs/2106.09681v2

[26] Shazeer N. GLU variants improve transformer[EB/OL]. http:∥arxiv.org/abs/2002.05202v1

[27] van den Oord A, Vinyals O, Kavukcuoglu K. Neural discrete representation learning[C], 6309-6318(2017).

[28] Sarukkai V, Jain A, Uzkent B et al. Cloud removal in satellite images using spatiotemporal generative networks[C], 1785-1794(2020).

[29] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 44, 800-801(2008).

[30] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2341-2353(2011).