Edge based Blind Single Image Deblurring with Sparse Priors

Khouloud Guemri, Fadoua Drira, Rim Walha, Adel M. Alimi, Frank LeBourgeois

2017

Abstract

Blind image deblurring is the estimation of the blur kernel and the latent sharp image from a blurry image. This makes it a significant ill-posed problem with various investigations looking for adequate solutions. The recourse to image priors have been noticed in recent approaches to improve final results. One of the most interesting results are based on data priors. This has been the starting point to the proposed blind image deblurring system. In particular, this study explores the potential of the sparse representation widely known for its efficiency in several reconstruction tasks. In fact, we propose a sparse representation based iterative deblurring method that exploits sparse constraints of edge based image patches. This process includes the K-SVD algorithm useful for the dictionary definition. Our main contributions are (1) the application of a shock filter as a pre-processing step followed by filter sub-bands applications for an effective contour detection, (2) the use of an online training data-sets with elementary patterns to describe edge-based information and (3) the recourse to an adaptative dictionary training. The experimental study illustrates promising results of the proposed deblurring method compared to the well-known state-of-the-art methods.

References

  1. Aharon, M., Elad, M., and Bruckstein, A. (2006). The ksvd: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, page 43114322.
  2. Cho, S. and Lee, S. (2009). Fast motion deblurring. ACM Transaction on Graphics, 28(5):145:1145:8.
  3. Cho, T. S., Paris, S., Horn, B. K. P., and Freeman, W. T. (2011). Blur kernel estimation using the radon transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  4. Elad, M., Figueiredo, M., and Ma, Y. (2010). On the role of sparse and redundant representations in image processing. Proceedings of the IEEE, 98(6):972982.
  5. Guemri, K. and Drira, F. (2014). Adaptative shock filter for image characters enhancement and denoising. International Conference of Soft Computing and Pattern Recognition (SoCPaR), 7(3):279-283.
  6. Hu, Z., Huang, J., and Yang, M. (2010). Single image deblurring with adaptive dictionary learning. International Conference on Image Processing (ICIP), page 11691172.
  7. Joshi, N., Szeliski, R., and Kriegman, D. (2008). Psf estimation using sharp edge prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  8. Lai, W., Ding, J., Lin, Y., and Chuang, Y. (2015). Blur kernel estimation using normalized color-line priors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, page 783798.
  9. Levin, A., Weiss, Y., Durand, F., and Freeman, W. T. (2011). Understanding blind deconvolution algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 33(12):23542367.
  10. Liu, Q., Liang, D., Song, Y., Luo, J., Zhu, Y., and Li, W. (2013). Augmented lagrangian-based sparse representation method with dictionary updating for image deblurring. SIAM Journal Imaging Science, page 1689C1718.
  11. Lou, Y., Bertozzi, A., and Soatto, S. (2011). Direct sparse deblurring. Journal of Mathematical Imaging and Vision, page 112.
  12. Michaeli, T. and Irani, M. (2014). Blind deblurring using internal patch recurrence. In Proceedings of the European Conference on Computer Vision, page 783798.
  13. Pan, J., Hu, Z., Su, Z., and Yang, M. (2014). Deblurring text images via l0-regularizedintensityandgradientprior. IEEE Conference on Computer Vision and Pattern Recognition.
  14. Shan, Q., Jia, J., and Agarwala, A. (2008). High-quality motion deblurring from a single image. ACM Special Interest Group on Computer Graphics (SIGGRAPH).
  15. Sun, L., Cho, S., Wang, J., and Hays, J. (2013). Edge-based blur kernel estimation using patch priors. in Proceedings of the IEEE International Conference on Computational Photography.
  16. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., and Alimi, A. (2015a). Joint denoising and magnification of noisy low-resolution textual images. International Conference on Document Analysis and Recognition (ICDAR), pages 871-875.
  17. Walha, R., Drira, F., Lebourgeois, F., Garcia, C., and Alimi, A. (2015b). Resolution enhancement of textual images via multiple coupled dictionaries and adaptive sparse representation selection. International Journal on Document Analysis and Recognition (IJDAR), 18(1):87-107.
  18. Xu, L. and Jia, J. (2010). Two-phase kernel estimation for robust motion deblurring. European Conference on Computer Vision (ECCV), 28(5):145:1145:8.
  19. Yu, G., Sapiro, G., and Mallat, S. (2010). Image modeling and enhancement via structured sparse model selection. International Conference on Image Processing (ICIP), page 16411644.
Download


Paper Citation


in Harvard Style

Guemri K., Drira F., Walha R., Alimi A. and LeBourgeois F. (2017). Edge based Blind Single Image Deblurring with Sparse Priors . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 174-181. DOI: 10.5220/0006129001740181


in Bibtex Style

@conference{visapp17,
author={Khouloud Guemri and Fadoua Drira and Rim Walha and Adel M. Alimi and Frank LeBourgeois},
title={Edge based Blind Single Image Deblurring with Sparse Priors},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={174-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006129001740181},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Edge based Blind Single Image Deblurring with Sparse Priors
SN - 978-989-758-225-7
AU - Guemri K.
AU - Drira F.
AU - Walha R.
AU - Alimi A.
AU - LeBourgeois F.
PY - 2017
SP - 174
EP - 181
DO - 10.5220/0006129001740181