EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 355 (2022) MATEC Web Conf., 355 (2022) 03005 References
Open Access
Issue
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
Article Number 03005
Number of page(s) 14
Section Computing Methods and Computer Application
DOI https://doi.org/10.1051/matecconf/202235503005
Published online 12 January 2022
  1. D. Gong, M. Tan, Y. Zhang, A. Van Den Hengel, Q. Shi, Self-paced kernel estimation for robust blind image deblurring, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1661–1670. [Google Scholar]
  2. X. Zhang, R. Wang, X. Jiang, W. Wang, W. Gao, Spatially variant defocus blur map estimation and deblurring from a single image, Journal of Visual Communication and Image Representation 35 (2016) 257–264. [CrossRef] [Google Scholar]
  3. L. He, Y. Wang, Z. Xiang, Support driven wavelet frame-based image deblurring, Information Sciences 479 (2019) 250–269. [CrossRef] [Google Scholar]
  4. X. Cai, Variational image segmentation model coupled with image restoration achievements, Pattern Recognition 48 (6) (2015) 2029–2042. [CrossRef] [Google Scholar]
  5. Q. Liu, L. Sun, Z. Shao, Nonblind image deblurring by total generalized variation and shearlet regularizations, Journal of Electronic Imaging 26 (5) (2017) 053021. [Google Scholar]
  6. T. Li, H. Chen, M. Zhang, S. Liu, S. Xia, X. Cao, G. S. Young, X. Xu, A new design in iterative image deblurring for improved robustness and performance, Pattern recognition 90 (2019) 134–146. [CrossRef] [Google Scholar]
  7. D. Krishnan, R. Fergus, Fast image deconvolution using hyper-laplacian priors, in: Advances in neural information processing systems, 2009, pp. 1033–1041. [Google Scholar]
  8. S. Wang, Z. Liu, W. Dong, L. Jiao, Q. Tang, Total variation based image deblurring with nonlocal self-similarity constraint, Electronics letters 47 (16) (2011) 916–918. [CrossRef] [Google Scholar]
  9. Y. Han, J. Kan, Blind color-image deblurring based on color image gradients, Signal Processing 155 (2019) 14–24. [CrossRef] [Google Scholar]
  10. Y. Bai, G. Cheung, X. Liu, W. Gao, Graph-based blind image deblurring from a single photograph, IEEE Transactions on Image Processing 28 (3) (2018) 1404–1418. [Google Scholar]
  11. J. Pan, J. Dong, Y.-W. Tai, Z. Su, M.-H. Yang, Learning discriminative data fifitting functions for blind image deblurring, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1068–1076. [Google Scholar]
  12. M. Ljubenovi´c, M. A. Figueiredo, Plug-and-play approach to class adapted blind image deblurring, International Journal on Document Analysis and Recognition (IJDAR) 22 (2) (2019) 79–97. [Google Scholar]
  13. T. Michaeli, M. Irani, Blind deblurring using internal patch recurrence, in: European Conference on Computer Vision, Springer, 2014, pp. 783–798. [Google Scholar]
  14. J. Zhu, K. Li, B. Hao, Hybrid variational model based on alternating direction method for image restoration, Advances in Difffference Equations 2019 (1) (2019) 34. [CrossRef] [Google Scholar]
  15. H. Yu, W. Wang, W. Fan, An adaptive iterative algorithm for motion deblurring based on salient intensity prior., TIIS 13 (2) (2019) 855–870. [Google Scholar]
  16. K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising, IEEE Transactions on Image Processing 26 (7) (2017) 3142–3155. [CrossRef] [Google Scholar]
  17. L. Ma, L. Xu, T. Zeng, Low rank prior and total variation regularization for image deblurring, Journal of Scientifific Computing 70 (3) (2017) 1336–1357. [CrossRef] [Google Scholar]
  18. J. Liu, M. Yan, T. Zeng, Surface-aware blind image deblurring, IEEE transactions on pattern analysis and machine intelligence. [Google Scholar]
  19. F. Wen, R. Ying, P. Liu, T.-K. Truong, Blind image deblurring using patch-wise minimal pixels regularization, arXiv preprint arXiv:1906.06642. [Google Scholar]
  20. J. Pan, R. Liu, Z. Su, X. Gu, Kernel estimation from salient structure for robust motion deblurring, Signal Processing: Image Communication 28 (9) (2013) 1156–1170. [CrossRef] [Google Scholar]
  21. X.-G. Lv, F. Li, T. Zeng, Convex blind image deconvolution with inverse filtering, Inverse Problems 34 (3) (2018) 035003. [CrossRef] [Google Scholar]
  22. Y. Liu, W. Lu, A robust iterative algorithm for image restoration, EURASIP Journal on Image and Video Processing 2017 (1) (2017) 53. [CrossRef] [Google Scholar]
  23. J. Pan, D. Sun, H. Pfifister, M.-H. Yang, Blind image deblurring using dark channel prior, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1628–1636. [Google Scholar]
  24. J. Pan, Z. Hu, Z. Su, M.-H. Yang, Deblurring text images via L0 regularized intensity and gradient prior, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2901–2908. [Google Scholar]
  25. D. Krishnan, T. Tay, R. Fergus, Blind deconvolution using a normalized sparsity measure, in: CVPR 2011, IEEE, 2011, pp. 233–240. [Google Scholar]
  26. L. Xu, J. Jia, Depth-aware motion deblurring, in: 2012 IEEE International Conference on Computational Photography (ICCP), IEEE, 2012, pp. 1–8. [Google Scholar]
  27. S. Zheng, L. Xu, J. Jia, Forward motion deblurring, in: Proceedings of the IEEE international conference on computer vision, 2013, pp. 1465–1472. [Google Scholar]
  28. O. Whyte, J. Sivic, A. Zisserman, J. Ponce, Non-uniform deblurring for shaken images, International journal of computer vision 98 (2) (2012) 168–186. [CrossRef] [Google Scholar]
  29. D. Wipf, H. Zhang, Revisiting bayesian blind deconvolution, Journal of Machine Learning Research (JMLR). [Google Scholar]
  30. S. Farsiu, M. D. Robinson, M. Elad, P. Milanfar, Fast and robust multiframe super resolution, IEEE transactions on image processing 13 (10) (2004) 1327–1344. [CrossRef] [Google Scholar]
  31. E. S. Lee, M. G. Kang, Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration, IEEE Trans actions on image processing 12 (7) (2003) 826–837. [CrossRef] [Google Scholar]
  32. R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, E. A. Watson, High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system, Optical Engineering 37 (1). [Google Scholar]
  33. D. A. Sorrentino, A. Antoniou, Improved hybrid demosaicing and color super-resolution implementation using quasi-newton algorithms, in: 2009 Canadian Conference on Electrical and Computer Engineering, IEEE, 2009, pp. 815–818. [CrossRef] [Google Scholar]
  34. L. Grippo, F. Lampariello, S. Lucidi, A nonmonotone line search technique for newton’s method, SIAM Journal on Numerical Analysis 23 (4) (1986) 707–716. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.