Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T. and Freeman, W.T. (2006). Removing camera shake from a single photograph, ACM Transactions on Graphics25(3): 787–794.10.1145/1141911.1141956
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 Science29(3): 611–621, DOI: 10.2478/amcs-2019-0045.10.2478/amcs-2019-0045
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.
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.
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.
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.
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.
Kotera, J., Smidl, V. and Sroubek, F. (2017). Blind deconvolution with model discrepancies, IEEE Transactions on Image Processing26(5): 2533–2544.10.1109/TIP.2017.267698128278468
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
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.
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.
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.
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.
Li, B. and Xie, W. (2015). Adaptive fractional differential approach and its application to medical image enhancement, Computers & Electrical Engineering45(C): 324–335.10.1016/j.compeleceng.2015.02.013
Li, B. and Xie, W. (2016). Image denoising and enhancement based on adaptive fractional calculus of small probability strategy, Neurocomputing175(Part A): 704 – 714.10.1016/j.neucom.2015.10.115
Li, J. and Lu, W. (2016). Blind image motion deblurring with ℓ0-regularized priors, Journal of Visual Communication & Image Representation40(Part A): 14–23.10.1016/j.jvcir.2016.06.003
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 Security14(8): 2000–2012.10.1109/TIFS.2018.2890812
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 Graphics32(6): 175:1–175:12.10.1145/2508363.2508391
Matychyn, I. and Onyshchenko, V. (2021). Time-optimal control of linear fractional systems with variable coefficients, International Journal of Applied Mathematics and Computer Science31(3): 375–386, DOI: 10.34768/amcs-2021-0025.
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
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
Pan, J., Sun, D., Pfister, H. and Yang, M. (2018). Deblurring images via dark channel prior, IEEE Transactions on Pattern Analysis and Machine Intelligence40(10): 2315–2328.10.1109/TPAMI.2017.275380428952935
Shan, Q., Jia, J. and Agarwala, A. (2008). High-quality motion deblurring from a single image, ACM Transactions on Graphics27(3): 1–10.10.1145/1360612.1360672
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.
Wang, H., Pan, J., Su, Z. and Liang, S. (2018). Blind image deblurring using elastic-net based rank prior, Computer Vision and Image Understanding168: 157–171.10.1016/j.cviu.2017.11.015
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.
Xie, Z. (2016). A primal-dual method with linear mapping for a saddle point problem in image deblurring, Journal of Visual Communication & Image Representation42: 112–120.10.1016/j.jvcir.2016.11.011
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.
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.
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.
Yin, M., Gao, J., Tien, D. and Cai, S. (2014). Blind image deblurring via coupled sparse representation, Journal of Visual Communication & Image Representation25(5): 814–821.10.1016/j.jvcir.2014.02.003
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.
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.