Journal of Information Processing Systems

  • 제18권1호
  • /
  • Pages.48-58
  • /
  • 2022
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

  • Liu, Ganghua (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Tian, Wei (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Luo, Yushun (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Zou, Juncheng (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Tang, Shu (College of Computer Science and Technology, Chongqing University of Posts and Telecommunications)
  • 투고 : 2021.07.13
  • 심사 : 2021.10.10
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

키워드

과제정보

All the authors will thank D. Krishnan, J. S. Pan, and L. Chen for offering their codes respectively.

참고문헌

  1. Q. Shan, J. Jia, and A. Agarwala, "High-quality motion deblurring from a single image," ACM Transactions on Graphics, vol. 27, no. 3, pp. 1-10, 2018.
  2. M. S. Almeida and L. B. Almeida, "Blind and semi-blind deblurring of natural images," IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 36-52, 2010. https://doi.org/10.1109/TIP.2009.2031231
  3. D. Krishnan, T. Tay, and R. Fergus, "Blind deconvolution using a normalized sparsity measure," in Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, 2011, pp. 233-240.
  4. L. Xu, S. Zheng, and J. Jia, "Unnatural l0 sparse representation for natural image deblurring," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, 2013, pp. 1107-1114).
  5. Z. Ma, R. Liao, X. Tao, L. Xu, J. Jia, and E. Wu, "Handling motion blur in multi-frame super-resolution," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 5224-5232.
  6. W. Zuo, D. Ren, D. Zhang, S. Gu, and L. Zhang, "Learning iteration-wise generalized shrinkage-thresholding operators for blind deconvolution," IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1751-1764, 2016. https://doi.org/10.1109/TIP.2016.2531905
  7. J. Pan, D. Sun, H. Pfister, and M. H. Yang, "Blind image deblurring using dark channel prior," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 1628-1636.
  8. J. Pan, Z. Hu, Z. Su, and M. H. Yang, "L0-regularized intensity and gradient prior for deblurring text images and beyond," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 2, pp. 342-355, 2017. https://doi.org/10.1109/TPAMI.2016.2551244
  9. Y. Guo and H. Ma, "Image blind deblurring using an adaptive patch prior," Tsinghua Science and Technology, vol. 24, no. 2, pp. 238-248, 2019. https://doi.org/10.26599/tst.2018.9010123
  10. X. Chen, R. Yang, C. Guo, S. Ge, Z. Wu, and X. Liu, "Hyper-Laplacian regularized non-local low-rank prior for blind image deblurring," IEEE Access, vol. 8, pp. 136917-136929, 2020. https://doi.org/10.1109/access.2020.3010540
  11. L. Chen, F. Fang, T. Wang, and G. Zhang, "Blind image deblurring with local maximum gradient prior," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 2019, pp. 1742-1750.
  12. H. Lim, S. Yu, K. Park, D. Seo, and J. Paik, "Texture-aware deblurring for remote sensing images using L0-based deblurring and L2-based fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3094-3108, 2020. https://doi.org/10.1109/jstars.2020.2999961
  13. J. Cai, W. Zuo, and L. Zhang, "Dark and bright channel prior embedded network for dynamic scene deblurring," IEEE Transactions on Image Processing, vol. 29, pp. 6885-6897, 2020. https://doi.org/10.1109/tip.2020.2995048
  14. J. Wu, X. Yu, D. Liu, M. Chandraker, and Z. Wang, "DAVID: dual-attentional video deblurring," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, 2020, pp. 2376-2385.
  15. K. Zhang, W. Luo, Y. Zhong, L. Ma, B. Stenger, W. Liu, and H. Li, "Deblurring by realistic blurring," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seatle, WA, 2020, pp. 2734-2743.
  16. A. Li, J. Li, Q. Lin, C. Ma, and B. Yan, "Deep image quality assessment driven single image deblurring," in Proceedings of 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK, 2020, pp. 1-6.
  17. W. Ren, J. Zhang, J. Pan, S. Liu, J. Ren, J. Du, X. Cao, and M. H. Yang, "Deblurring dynamic scenes via spatially varying recurrent neural networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. https://doi.org/10.1109/TPAMI.2021.3061604
  18. D. Ren, W. Zuo, D. Zhang, L. Zhang, and M. H. Yang, "Simultaneous fidelity and regularization learning for image restoration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 284-299, 2021. https://doi.org/10.1109/TPAMI.2019.2926357
  19. Y. Hu, J. Li, Y. Huang, and X. Gao, "Channel-wise and spatial feature modulation network for single image super-resolution," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 11, pp. 3911-3927, 2020. https://doi.org/10.1109/tcsvt.2019.2915238
  20. B. Lu, J. C. Chen, and R. Chellappa, "UID-GAN: unsupervised image deblurring via disentangled representations," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 1, pp. 26-39, 2020. https://doi.org/10.1109/tbiom.2019.2959133
  21. L. Xu, Q. Yan, Y. Xia, and J. Jia, "Structure extraction from texture via relative total variation," ACM Transactions on Graphics, vol. 31, no. 6, article no. 139, 2012. https://doi.org/10.1145/2366145.2366158
  22. A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, "Understanding and evaluating blind deconvolution algorithms," in Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 2009, pp. 1964-1971.
  23. R. Kohler, M. Hirsch, B. Mohler, B. Scholkopf, and S. Harmeling, "Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database," in Computer Vision - ECCV 2012. Heidelberg, Germany: Springer, 2012, pp. 27-40.
  24. L. Sun, S. Cho, J. Wang, and J. Hays, "Edge-based blur kernel estimation using patch priors," in Proceedings of IEEE International Conference on Computational Photography (ICCP), Cambridge, MA, 2013, pp. 1-8.