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References

1 
A. Levin, Y. Weiss, F. Durand, W. T. Freeman, “Efficient marginal likelihood optimization in blind deconvolution,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2011. DOI:10.1109/CVPR.2011.5995308DOI
2 
S. Cho and S. Lee, “Fast motion deblurring,” ACM Transactions on Graphics, vol. 28, pp. 1-8, 2009. DOI:10.1145/1618452.1618491DOI
3 
W. Zuo, D. Ren, D. Zhang, D., S. Gu, S. and L. Zhang, “Learning iteration-wise generalized shrinkage–thresholding operators for blind deconvolution,” IEEE Transactions on Image Processing, vol. 25, pp. 1751-1764, 2016. DOI:10.1109/TIP.2016.2531905DOI
4 
D. Perrone, P. Favaro, “A clearer picture of total variation blind deconvolution,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, pp. 1041-1055, 2016. DOI:10.1109/TPAMI.2015.2477819DOI
5 
W. S. Lai, J. B. Huang, Z. Hu, Z. Ahuja and M. H. Yang, “A comparative study for single image blind deblurring,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701-1709, 2016. DOI:10.1109/CVPR.2016.188DOI
6 
O. Elharrouss, N. Almaadeed, S. A. Maadeed and Y. Akbari, “Image inpainting: A review,” Neural Processing, vol. 51, pp. 2007-2028, 2020. DOI:10.1007/s11063-019-10163-0DOI
7 
K. Zhang, W. Zuo, Y. Chen, D. Meng and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing, vol. 26, pp. 3142-3155, 2017. DOI:10.1109/TIP.2017.2662206DOI
8 
S. Sahu, M. K. Lenka, P. K. Sa, “Blind deblurring using deep learning: a survey,” arXiv:1907.10128, 2019.URL
9 
J. Zhang, J. Pan, W. S. Lai, R. W. H. Lau and M. H. Yang, “Learning fully convolutional networks for iterative non-blind deconvolution,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3817-3825, 2017. DOI:10.1109/CVPR.2017.737DOI
10 
L. Xu, J.S. Ren, C. Liu and J. Jia, “Deep convolutional neural network for image deconvolution,” 27th Int. Conf. Neural Inf. Process. Syst., pp. 1790-1798, 2014.URL
11 
R. Yan and L. Shao, “Blind image blur estimation via deep learning,” IEEE Trans. Image Process., vol. 25, pp. 1910-1921, 2016. DOI:10.1109/TIP.2016.2535273DOI
12 
J. Zhang, J. Pan, J. Ren, Y. Song, L. Bao, R. W. H. Lau, M. H. Yang, “Dynamic scene deblurring using spatially variant recurrent neural networks,” IEEE Conf. Comput. Vision Pattern Recognit., pp. 2521-2529, 2018. DOI:10.1109/CVPR.2018.00267DOI
13 
X. Tao, H. Gao, X. Shen, J. Wang, J. Jia, “Scale-recurrent network for deep image deblurring,” IEEE Conf. Comput Vision Pattern Recognit., pp. 8174-8182, 2018. DOI:10.1109/CVPR.2018.00853DOI
14 
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin and J. Matas, “DeblurGAN: blind motion deblurring using conditional adversarial networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Reconition (CVPR), pp. 8183-8192, 2018. DOI:10.1109/cvpr.2018.00854DOI
15 
C. Agarwal, S. Khobahi, A. Bose, M. Soltanalian, D. Schonfeld, “Deep-URL: a model-aware approach to blind deconvolution based on deep unfolded Richardson-Lucy network,” IEEE International Conference on Image Processing, pp. 25-28, Oct. 2020. DOI:10.48550/arXiv.2002.01053DOI
16 
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 (CVPR), pp. 3883-3891, 2017. DOI:10.1109/cvpr.2017.35DOI
17 
J. Zhang, J. Pan, J. Ren, Y. Song, L. Bao, R. W. H. Lau, M. H. Yang, “Dynamic scene deblurring using spatially variant recurrent neural networks,” IEEE Conf. Comput. Vision Pattern Recognition, pp. 2521-2529, 2018. DOI:10.1109/CVPR.2018.00267DOI
18 
W. Jeong, S. Kim and C. Lee, “New U-Net for image deblurring using deep learning,” Transactions of the Korean Institute of Electrical Engineers, vol. 72, pp. 843-848, 2023. DOI:10.5370/KIEE.2023.72.7.843DOI
19 
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, M. H. Yang, “Restormer: efficient transformer for high-resolution image restoration,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5728-5739, 2022. DOI:10.1109/CVPR52688.2022.00564DOI
20 
Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, H. Li, “Uformer: a general U-shaped transformer for image restoration,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17683-17693, 2022. DOI:10.1109/CVPR52688.2022.01716DOI
21 
Z. Jin, Y. Qiu, K. Zhang, H. Li and W. Luo, “MB- TaylorFormer v2: improved multi-branch linear transformer expanded by Taylor formula for image restoration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, pp. 5990-6005, 2025. DOI:10.1109/TPAMI.2025.3559891DOI
22 
K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016. DOI:10.1109/CVPR.2016.90DOI
23 
F. He, T. Liu and D. Tao, “Why ResNet works? residuals generalize,” IEEE Trans. on Neural Networks and Learning Syetems, vol. 31, pp. 5349-5362, 2020. DOI:10.1109/TNNLS.2020.2966319DOI
24 
O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015. DOI:10.1007/978-3-319-24574-4_28DOI
25 
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” Proceedings of the 32nd International Conference on Machine Learning, pp. 448-456, 2015.URL
26 
J. L. Ba, J. R. Kiros and G. E. Hinton, “Layer normalization,” arXiv:1607.06450, 2016.URL
27 
D. Ulyanov, A. Vedaldi and V. Lempitsky, “Instance normalization: the missing ingredient for fast stylization,” arXiv:1607.08022, 2016.URL
28 
Y. Wu and K. He, “Group normalization,” Proceedings of the European Conference on Computer Vision (ECCV), pp. 3-19, 2018. DOI:10.1007/978-3-030-01261-8_1DOI
29 
H. Liu, A. Brock, K. Simonyan and Q. Le, “Evolving normalization activation layers,” Proc. Int. Conf. Neural Inf. Process. Syst., pp. 13539-13550, 2020.URL
30 
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai and S. Chintala, “PyTorch: An imperative style, high-performance deep learning library,” Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019.DOI
31 
D. Kingma, J. B. Adam, “Adam: a method for stochastic optimization,” arXiv:1412.6980, 2014.URL
32 
A. Horé and D. Ziou, “Image Quality Metrics: PSNR vs. SSIM,” International Conference on Pattern Recognition 2010, pp. 2366-2369, 2010. DOI:10.1109/icpr.2010.579DOI