cvpr cvpr2013 cvpr2013-265 cvpr2013-265-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Florent Couzinié-Devy, Jian Sun, Karteek Alahari, Jean Ponce
Abstract: This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion. The estimation of the global (non-uniform) image blur is cast as a multilabel energy minimization problem. The energy is the sum of unary terms corresponding to learned local blur estimators, and binary ones corresponding to blur smoothness. Its global minimum is found using Ishikawa ’s method by exploiting the natural order of discretized blur values for linear motions and defocus. Once the blur has been estimated, the image is restored using a robust (non-uniform) deblurring algorithm based on sparse regularization with global image statistics. The proposed algorithm outputs both a segmentation of the image into uniform-blur layers and an estimate of the corresponding sharp image. We present qualitative results on real images, and use synthetic data to quantitatively compare our approach to the publicly available implementation of Chakrabarti et al. [5].
[1] L. Bar, B. Berkels, M. Rumpf, and G. Sapiro. A variational framework for simultaneous motion estimation and restoration of motion-blurred video. In ICCV, 2007.
[2] E. Boros and P. L. Hammer. Pseudo-boolean optimization. Discrete applied mathematics, 123(1-3): 155–225, 2002.
[3] Y. Boykov and M.-P. Jolly. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In ICCV, volume 1, pages 105–1 12, 2001.
[4] J. F. Cai, H. Ji, C. Liu, and Z. Shen. Blind motion deblurring from a single image using sparse approximation. In CVPR, pages 104–1 11, 2009.
[5] A. Chakrabarti, T. Zickler, and W. T. Freeman. Analyzing spatially-varying blur. In CVPR, pages 25 12–25 19, 2010.
[6] S. Cho, H. Cho, Y. W. Tai, and S. Lee. Registration based non-uniform motion deblurring. Computer Graphics Forum, 31(7):2183–2192, 2012.
[7] S. Cho and S. Lee. Fast motion deblurring. ACM Transactions on Graphics, 28(5): 145, 2009.
[8] S. Cho, Y. Matsushita, and S. Lee. Removing non-uniform motion blur from images. In ICCV, 2007.
[9] S. Dai and Y. Wu. Motion from blur. In CVPR, 2008.
[10] J. Darbon. Global optimization for first order markov random fields with submodular priors. Discrete Applied Mathematics, 157(16):3412–3423, 2009.
[11] M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Processing, 15(12):3736–3745, 2006.
[12] R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. Liblinear: A library for large linear classification. Jour- nal of Machine Learning Research, 9: 1871–1874, 2008. 11111000007778880088 Figure 7. Horizontal blur: Sample deblurring results on two real images from [5] and on one synthetic image. From left image, deblurred image, close-up corresponding to the boxes shown in red. (Best viewed in pdf.) to right: blurry
[13] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. ACM Transactions on Graphics, 25(3):787–794, 2006.
[14] D. Geman and C. Yang. Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Processing, 4(7):932–946, 1995.
[15] A. Goldstein and R. Fattal. Blur-kernel estimation from spectral irregularities. In ECCV, pages 622–635, 2012.
[16] A. Gupta, N. Joshi, C. L. Zitnick, M. Cohen, and B. Curless. Single image deblurring using motion density functions. In ECCV, pages 171–184, 2010.
[17] M. Hirsch, C. J. Schuler, S. Harmeling, and B. Scholkopf. Fast removal of non-uniform camera shake. In ICCV, pages 463–470, 2011.
[18] Z. Hu and M.-H. Yang. Good regions to deblur. In ECCV, volume V, pages 59–72, 2012.
[19] H. Ishikawa. Exact optimization for markov random fields with convex priors. IEEE Trans. PAMI, 25(10): 1333–1336, 2003.
[20] H. Ji and K. Wang. Robust image deblurring with an inaccurate blur kernel. IEEE Trans. Image Processing, 21(4): 1624– 1634, 2012.
[21] H. Ji and K. Wang. A two-stage approach to blind spatiallyvarying motion deblurring. In CVPR, pages 73–80, 2012.
[22] N. Joshi, R. Szeliski, and D. J. Kriegman. Psf estimation using sharp edge prediction. In CVPR, 2008.
[23] D. Krishnan and R. Fergus. Fast image deconvolution using hyper-laplacian priors. In NIPS, pages 1033–1041, 2009.
[24] D. Krishnan, T. Tay, and R. Fergus. Blind deconvolution using a normalized sparsity measure. In CVPR, pages 233– 240, 2011.
[25] A. Levin. Blind motion deblurring using image statistics. In NIPS, pages 841–848, 2007.
[26] A. Levin, R. Fergus, F. Durand, and W. T. Freeman. Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics, 26(3):70, 2007. 1 1 10 0 07 78 91 9 Figure 8. Defocus blur: Sample deblurring results on real images. From left to right: blurry image, deblurred image, close-up corresponding to the boxes shown in red. Note that our estimated deblurred image has more detail. (Best viewed in pdf.)
[27] A. Levin, Y. Weiss, F. Durand, and W. T. Freeman. Understanding and evaluating blind deconvolution algorithms. In CVPR, pages 1964–1971, 2009.
[28] R. Liu, Z. Li, and J. Jia. Image partial blur detection and classification. In CVPR, 2008.
[29] L. B. Lucy. An iterative technique for the rectification of observed distributions. Astronomical Journal, 79:745–754, 1974.
[30] D. R. Martin, C. C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. PAMI, 26(5):530–549, 2004. [3 1] J. Nocedal. Updating quasi-newton matrices with limited storage. Mathematics of computation, 35(15 1):773–782, 1980.
[32] W. H. Richardson. Bayesian-based iterative method of image restoration. JOSA, 62(1):55–59, 1972.
[33] Q. Shan, J. Jia, and A. Agarwala. High-quality motion de-
[34]
[35]
[36]
[37]
[38] blurring from a single image. ACM Transactions on Graphics, 27(3):73, 2008. Y. W. Tai, P. Tan, and M. S. Brown. Richardson-lucy deblurring for scenes under a projective motion path. IEEE Trans. PAMI, 33(8): 1603–1618, 2011. O. Whyte, J. Sivic, A. Zisserman, and J. Ponce. Non-uniform deblurring for shaken images. International Journal of Computer Vision, 98(2): 168–186, 2012. L. Xu and J. Jia. Two-phase kernel estimation for robust motion deblurring. In ECCV, volume I, pages 157–170, 2010. L. Yuan, J. Sun, L. Quan, and H. Y. Shum. Image deblurring with blurred/noisy image pairs. ACM Transactions on Graphics, 26(3): 1, 2007. X. Zhu, F. Sroubek, and P. Milanfar. Deconvolving psfs for a better motion deblurring using multiple images. In ECCV, volume V, pages 636–647, 2012. 11111000008888802200