Abstract
For blind image deblurring, a good prior knowledge can guide the maximum a posterior (MAP) based algorithms to be away from the trivial solution. Therefore, many existing methods focus on designing effective priors to constrain the solution space. However, blind deconvolution with fixed priors is not robust. And many priors are extremely costly to design and compute. In this paper, we proposed a blind deconvolution method with adaptive priors under the MAP framework. Specifically, we carry out our algorithm under the multi-scale, and at each scale we add specific sparse regularization to standard deblurring formulation. By tunning both the priors and the weights we can give more flexible sparse regularization constraint. After iteration, our algorithm output both latent image and estimated blur kernel, simultaneously. We prove the convergence of the proposed algorithm. Extensive experiments show the effectiveness of our proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Alternating direction method of multiplier.
References
Buades, A., Coll, B., Morel, J.M.: Non-local means denoising. Image Process. Line 1, 208–212 (2011)
Chan, T.F., Wong, C.K.: Total variation blind deconvolution. IEEE Trans. Image Process. 7(3), 370–375 (1998)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (TOG) 28, 145 (2009)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. (TOG) 25, 787–794 (2006)
Foi, A.: Image and video denoising by sparse 3D transform-domain collaborative filtering. Transforms and Spectral Methods Group, Department of Signal Processing, Tampere University (2014). http://www.cs.tut.fi/foi/GCF-BM3D/. Accessed 3 Aug 2014
Hu, Z., Yang, M.-H.: Good regions to deblur. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 59–72. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_5
Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.: Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 27–40. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33786-4_3
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 233–240. IEEE (2011)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1964–1971. IEEE (2009)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2657–2664. IEEE (2011)
Nie, L., Yan, S., Wang, M., Hong, R., Chua, T.S.: Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 59–68. ACM (2012)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. (TOG) 27, 73 (2008)
Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: Proceedings of the IEEE International Conference on Computational Photography (2013)
Wang, Y., Yin, W.: Compressed sensing via iterative support detection. Rice University CAAM Technical report TR09-30 (2009)
Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_12
Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1107–1114 (2013)
Zhang, H., Zha, Z.J., Yang, Y., Yan, S., Gao, Y., Chua, T.S.: Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 33–42. ACM (2013)
Zuo, W., Ren, D., Gu, S., Lin, L., Zhang, L.: Discriminative learning of iteration-wise priors for blind deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3232–3240 (2015)
Acknowledgement
This work was partially supported by National Natural Science Funds of China (61472059, 61632019 and 61672125).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, B., Liu, R., Li, H., Yuan, Q., Fan, X., Luo, Z. (2018). Blind Image Deblurring Using Adaptive Priors. In: Huet, B., Nie, L., Hong, R. (eds) Internet Multimedia Computing and Service. ICIMCS 2017. Communications in Computer and Information Science, vol 819. Springer, Singapore. https://doi.org/10.1007/978-981-10-8530-7_2
Download citation
DOI: https://doi.org/10.1007/978-981-10-8530-7_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8529-1
Online ISBN: 978-981-10-8530-7
eBook Packages: Computer ScienceComputer Science (R0)