[1] F. Chen, J. Sun, Q. Wang et al. In-situ laser-induced surface damage inspection based on image super-resolution and adaptive segmentation method. Chin. Opt. Lett., 20, 071101(2022).
[2] L. Wang, G. Li, X. Kang. Towards super-resolution via iterative multi-exposure coaddition. Mon. Not. R. Astron. Soc., 517, 787(2022).
[3] M. Guo, Y. Li, Y. Su et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat. Biotechnol., 38, 1337(2020).
[4] P. H. C. Eilers, C. Ruckebusch. Fast and simple super‑resolution with single images. Sci. Rep., 12, 11241(2022).
[5] P. Wijesinghe, S. Corsetti, D. J. X. Chow et al. Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams. Light Sci. Appl., 11, 319(2022).
[6] Y. Nan, H. Ji. Deep learning for handling kernel/model uncertainty in image deconvolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2020).
[7] J. Dong, S. Roth, B. Schiele. DWDN: deep Wiener deconvolution network for non-blind image deblurring. IEEE Trans. Pattern Anal. Mach. Intell., 44, 9960(2021).
[8] Z. Wang, X. Cun, J. Bao et al. Uformer: a general U-shaped transformer for image restoration. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(2022).
[9] L. Chen, X. Chu, X. Zhang et al. Simple baselines for image restoration. Computer Vision–ECCV 2022(2022).
[10] S. Nah, T. H. Kim, K. M. Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 257(2017).
[11] S.-J. Cho, S. W. Ji, J. P. Hong et al. Rethinking coarse-to-fine approach in single image deblurring. Proceedings of the IEEE/CVF International Conference on Computer Vision(2021).
[12] M. Delbracio, P. Musé, A. Almansa. Non-parametric sub-pixel local point spread function estimation. Image Process. Line, 2, 8(2012).