[1] N. Ji. Adaptive optical fluorescence microscopy. Nat. Methods, 14, 374-380(2017).
[10] Y. Zhang, K. P. Li, K. Li. Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), 294-310(2018).
[11] O. Ronneberger, P. Fischer, T. Brox. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 1-8(2015).
[12] J. Gu, H. Lu, W. Zuo. Blind super-resolution with iterative kernel correction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1604-1613(2019).
[13] D. Hendrycks, K. Gimpel. Gaussian error linear units (GELUs). arXiv(2016).
[14] J. Caballero, C. Ledig, A. Aitken. Real-time video super-resolution with spatio-temporal networks and motion compensation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4778-4787(2017).
[16] A. Liu, Y. Liu, J. Gu. Blind image super-resolution: a survey and beyond. arXiv(2021).
[17] J. Liang, G. Sun, K. Zhang. Mutual affine network for spatially variant kernel estimation in blind image super-resolution. Proceedings of the IEEE/CVF International Conference on Computer Vision, 4096-4105(2021).
[19] D. Ren, K. Zhang, Q. Wang. Neural blind deconvolution using deep priors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3341-3350(2020).
[20] J. Liang, K. Zhang, S. Gu. Flow-based kernel prior with application to blind super-resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10601-10610(2021).
[23] Z. Liu, Y. Lin, Y. Cao. Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, 10012-10022(2021).
[24] https://github.com/HypnosRin/SFE-Net. https://github.com/HypnosRin/SFE-Net