[
An, S., Roh, H. and Kang, M. (2020). Long-term residual blending network for blur invariant single image blind deblurring, arXiv: 2007.04543.
]Search in Google Scholar
[
Cho, S. and Lee, S. (2009). Fast motion deblurring, ACM SIGGRAPH Asia 28(5): 1–8.10.1145/1618452.1618491
]Search in Google Scholar
[
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T. and Freeman, W.T. (2006). Removing camera shake from a single photograph, ACM Transactions on Graphics 25(3): 787–794.10.1145/1141911.1141956
]Search in Google Scholar
[
Fuhai, C., Rongrong, J., Chengpeng, D., Xiaoshuai, S., Chia-Wen, L., Jiayi, J., Baochang, Z., Feiyue, H. and Liujuan, C. (2019). Semantic-aware image deblurring, arXiv: 1910.03853.
]Search in Google Scholar
[
Gao, D., Liu, J., Wu, R., Cheng, D., Fan, X. and Tang, X. (2019). Utilizing relevant RGB-D data to help recognize RGB images in the target domain, International Journal of Applied Mathematics and Computer Science 29(3): 611–621, DOI: 10.2478/amcs-2019-0045.10.2478/amcs-2019-0045
]Search in Google Scholar
[
Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I., Shen, C., Van Den Hengel, A. and Shi, Q. (2017). From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, pp. 3806–3815.
]Search in Google Scholar
[
He, K., Sun, J. and Tang, X. (2009). Single image haze removal using dark channel prior, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 1956–1963.
]Search in Google Scholar
[
Jia, H. and Pu, Y. (2008). Fractional calculus method for enhancing digital image of bank slip, 2008 Congress on Image and Signal Processing, Sanya, China, Vol. 3, pp. 326–330.
]Search in Google Scholar
[
Joshi, N., Zitnick, C.L., Szeliski, R. and Kriegman, D.J. (2009). Image deblurring and denoising using color priors, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 1550–1557.
]Search in Google Scholar
[
Kohler, R., Hirsch, M., Mohler, B., Scholkopf, B. and Harmeling, S. (2012). Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database, 2012 European Conference on Computer Vision, Florence, Italy, pp. 27–40.
]Search in Google Scholar
[
Kotera, J., Smidl, V. and Sroubek, F. (2017). Blind deconvolution with model discrepancies, IEEE Transactions on Image Processing 26(5): 2533–2544.10.1109/TIP.2017.267698128278468
]Search in Google Scholar
[
Kotera, J., Šroubek, F. and Milanfar, P. (2013). Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors, in R. Wilson et al. (Eds), Computer Analysis of Images and Patterns, Springer, Berlin/Heidelberg, pp. 59–66.10.1007/978-3-642-40246-3_8
]Search in Google Scholar
[
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D. and Matas, J. (2018). DeblurGAN: Blind motion deblurring using conditional adversarial networks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 8183–8192.
]Search in Google Scholar
[
Lai, W.-S., Ding, J.-J., Lin, Y.-Y. and Chuang, Y.-Y. (2015). Blur kernel estimation using normalized color-line priors, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, pp. 64–72.
]Search in Google Scholar
[
Lai, W.S., Huang, J.B., Hu, Z., Ahuja, N. and Yang, M.H. (2016). A comparative study for single image blind deblurring, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, pp. 1701–1709.
]Search in Google Scholar
[
Levin, A., Weiss, Y., Durand, F. and Freeman, W.T. (2009). Understanding and evaluating blind deconvolution algorithms, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 1964–1971.
]Search in Google Scholar
[
Levin, A., Weiss, Y., Durand, F. and Freeman, W.T. (2011). Efficient marginal likelihood optimization in blind deconvolution, 2011 IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, USA, pp. 2657–2664.
]Search in Google Scholar
[
Li, B. and Xie, W. (2015). Adaptive fractional differential approach and its application to medical image enhancement, Computers & Electrical Engineering 45(C): 324–335.10.1016/j.compeleceng.2015.02.013
]Search in Google Scholar
[
Li, B. and Xie, W. (2016). Image denoising and enhancement based on adaptive fractional calculus of small probability strategy, Neurocomputing 175(Part A): 704 – 714.10.1016/j.neucom.2015.10.115
]Search in Google Scholar
[
Li, J. and Lu, W. (2016). Blind image motion deblurring with ℓ0-regularized priors, Journal of Visual Communication & Image Representation 40(Part A): 14–23.10.1016/j.jvcir.2016.06.003
]Search in Google Scholar
[
Li, P., Prieto, L., Mery, D. and Flynn, P.J. (2019). On low-resolution face recognition in the wild: Comparisons and new techniques, IEEE Transactions on Information Forensics and Security 14(8): 2000–2012.10.1109/TIFS.2018.2890812
]Search in Google Scholar
[
Liu, Y., Wang, J., Cho, S., Finkelstein, A. and Rusinkiewicz, S. (2013). A no-reference metric for evaluating the quality of motion deblurring, ACM Transactions on Graphics 32(6): 175:1–175:12.10.1145/2508363.2508391
]Search in Google Scholar
[
Matychyn, I. and Onyshchenko, V. (2021). Time-optimal control of linear fractional systems with variable coefficients, International Journal of Applied Mathematics and Computer Science 31(3): 375–386, DOI: 10.34768/amcs-2021-0025.
]Search in Google Scholar
[
Pan, J., Hu, Z., Su, Z. and Yang, M.H. (2014a). Deblurring text images via ℓ0-regularized intensity and gradient prior, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 2901–2908.10.1109/CVPR.2014.371
]Search in Google Scholar
[
Pan, J., Liu, R., Su, Z. and Liu, G. (2014b). Motion blur kernel estimation via salient edges and low rank prior, 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China, pp. 1–6.10.1109/ICME.2014.6890182
]Search in Google Scholar
[
Pan, J., Sun, D., Pfister, H. and Yang, M. (2018). Deblurring images via dark channel prior, IEEE Transactions on Pattern Analysis and Machine Intelligence 40(10): 2315–2328.10.1109/TPAMI.2017.275380428952935
]Search in Google Scholar
[
Ren, W., Cao, X., Pan, J., Guo, X., Zuo, W. and Yang, M.H. (2016). Image deblurring via enhanced low-rank prior, IEEE Transactions on Image Processing 25(7): 3426–3437.10.1109/TIP.2016.257106228113429
]Search in Google Scholar
[
Shan, Q., Jia, J. and Agarwala, A. (2008). High-quality motion deblurring from a single image, ACM Transactions on Graphics 27(3): 1–10.10.1145/1360612.1360672
]Search in Google Scholar
[
Sun, L., Cho, S., Wang, J. and Hays, J. (2013). Edge-based blur kernel estimation using patch priors, IEEE International Conference on Computational Photography (ICCP), Cambridge, USA, pp. 1–8.
]Search in Google Scholar
[
Wang, H., Pan, J., Su, Z. and Liang, S. (2018). Blind image deblurring using elastic-net based rank prior, Computer Vision and Image Understanding 168: 157–171.10.1016/j.cviu.2017.11.015
]Search in Google Scholar
[
Wang, Z., Simoncelli, E.P. and Bovik, A.C. (2003). Multiscale structural similarity for image quality assessment, 37th Asilomar Conference on Signals, Systems Computers, Pacific Grove, USA, Vol. 2, pp. 1398–1402.
]Search in Google Scholar
[
Chen, X., Yang, Q.W. and Wu, J. (2010). Image deblur in gradient domain, Optical Engineering 49(11): 49–49–7.10.1117/1.3505868
]Search in Google Scholar
[
Xie, Z. (2016). A primal-dual method with linear mapping for a saddle point problem in image deblurring, Journal of Visual Communication & Image Representation 42: 112–120.10.1016/j.jvcir.2016.11.011
]Search in Google Scholar
[
Xu, L. and Jia, J. (2010). Two-phase kernel estimation for robust motion deblurring, Proceedings of the 11th European Conference on Computer Vision: ECCV’10, Heraklion, Crete, Greece, Part I, pp. 157–170.
]Search in Google Scholar
[
Xu, L., Zheng, S. and Jia, J. (2013). Unnatural L0 sparse representation for natural image deblurring, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, pp. 1107–1114.
]Search in Google Scholar
[
Yan, Y., Ren, W., Guo, Y., Wang, R. and Cao, X. (2017). Image deblurring via extreme channels prior, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, pp. 6978–6986.
]Search in Google Scholar
[
Yin, M., Gao, J., Tien, D. and Cai, S. (2014). Blind image deblurring via coupled sparse representation, Journal of Visual Communication & Image Representation 25(5): 814–821.10.1016/j.jvcir.2014.02.003
]Search in Google Scholar
[
Zhang, H., Dai, Y., Li, H. and Koniusz, P. (2019). Deep stacked hierarchical multi-patch network for image deblurring, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, pp. 5971–5979.
]Search in Google Scholar
[
Zhichao, F., Yingbin, Z., Hao, Y., Yu, K., Jing, Y. and Liang, H. (2019). Edge-aware deep image deblurring, arXiv: 1907.02282.
]Search in Google Scholar
[
Zhong, L., Cho, S., Metaxas, D., Paris, S. and Wang, J. (2013). Handling noise in single image deblurring using directional filters, 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, pp. 612–619.
]Search in Google Scholar