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Image Deblurring using L0 Sparse and Directional Filters
Aparna Ashok, Deepa P. L.
Pages - 209 - 221     |    Revised - 30-06-2015     |    Published - 31-07-2015
Volume - 9   Issue - 4    |    Publication Date - July / August 2015  Table of Contents
Motion Blur, Blind Deconvolution, Deblurring, L0 Sparsity, Directional Filtering, Image Restoration.
Blind deconvolution refers to the process of recovering the original image from the blurred image when the blur kernel is unknown. This is an ill-posed problem which requires regularization to solve. The naive MAP approach for solving the blind deconvolution problem was found to favour no-blur solution which in turn led to its failure. It is noted that the success of the further developed successful MAP based deblurring methods is due to the intermediate steps in between, which produces an image containing only salient image structures. This intermediate image is essentially called the unnatural representation of the image. L0 sparse expression can be used as the regularization term to effectively develop an efficient optimization method that generates unnatural representation of an image for kernel estimation. Further, the standard deblurring methods are affected by the presence of image noise. A directional filter incorporated as an initial step to the deblurring process makes the method efficient to be used for blurry as well as noisy images. Directional filtering along with L0 sparse regularization gives a good kernel estimate in spite of the image being noisy. In the final image restoration step, a method to give a better result with lesser artifacts is incorporated. Experimental results show that the proposed method recovers a good quality image from a blurry and noisy image.
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1 D. Kundur and D. Hatzinakos. "Blind image deconvolution", IEEE Signal Processing Magazine, 1996.
2 A. Levin, Y. Weiss, F. Durand and W. T. Freeman. "Understanding and evaluating blind deconvolution algorithms." In CVPR,2009, pp.1964–1971.
3 R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis and W. T. Freeman." Removing camera shake from a single photograph." ACM Transactions on Graphics (SIGGRAPH ASIA), vol. 28 no.5, p. article no. 787–794, 2006.
4 Q. Shan, J. Jia, and A. Agarwala. "High-quality motion deblurring from a single image."ACM Transactions on Graphics (SIGGRAPH ASIA),vol. 27(3),2008.
5 J. H. Money and S. H. Kang. "Total variation minimizing blind deconvolution with shock filter reference." Image and Vision Computing, 26(2):302–314, 2008.
6 S. Cho and S. Lee. "Fast motion deblurring." ACM Transactions on Graphics (SIGGRAPH ASIA), vol. 28, no. 5, p. article no. 145, Dec. 2009.
7 D. Krishnan, T. Tay, and R. Fergus. "Blind deconvolution using a normalized sparsity measure." In CVPR, 2011,pp. 233–240.
8 L. Xu, S. Zheng and J. Jia. "Unnatural l0 sparse representation for natural image deblurring." In CVPR, 2013,pp. 1107–1114.
9 R. Koehler, M. Hirsch, S. Harmeling, B. Mohler and B.Scholkopf. "Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database." In ECCV, 2012, pp. 27–40.
10 A. Levin, Y. Weiss, F. Durand, and W. T. Freeman. "Efficient marginal likelihood optimization in blind deconvolution." In CVPR2011,pp. 2657–2664.
11 A. Buades, B. coll and J. Morel." A non-local algorithm for image denoising." CVPR, 2005,pp. 60-65.
12 L. Zhang, A. Deshpande and X. Chen. "Denoising vs. deblurring: Hdr imaging techniques using moving cameras." In CVPR,2010 pp. 522-529.
13 L. Zhong, S. Cho, D. Metaxas, S. Paris, and J. Wang. "Handling noise in single image deblurring using directional filters",In CVPR,2013, pp. 612–619.
14 L. Xu and J. Jia." Two-phase kernel estimation for robust motion deblurring”, ECCV, 2010, pp. 157–170.
15 L. Xu, Q. Yan, Y. Xia and J. Jia. "Structure extraction from texture via relative total variation." ACM Transactions on Graphics (SIGGRAPH ASIA), vol. 31(6), 2012.
16 J. Jia. "Single image motion deblurring using transparency." In CVPR, 2007, pp. 1-8.
17 N. Joshi, R. Szeliski and D. J. Kriegman."PSF estimation using sharp edge prediction." In CVPR, 2008, pp. 1-8.
18 T. S. Cho, S. Paris, B. K. P. Horn and W. T. Freeman. "Blur kernel estimation using the radon transform." In CVPR, 2011,pp. 241–248.
19 M. Hirsch, C. J. Schuler, S. Harmeling and B. Scholkopf. "Fast removal of non-uniform camera shake." In ICCV, 2011,pp. 463–470.
20 A. Buades, C. B. and J.-M. Morel. "The stair casing effect in neighborhood filters and its solution." IEEE Transaction on Image Processing,vol.15,pp. 1499-1505, 2006.
21 D. Krishnan and R. Fergus."Fast image deconvolution using hyper-laplacian priors." In NIPS, pp. 1033-1041,2009.
22 Z.Hu, M.H.Yang, J.Pan and Z.Su. "Deblurring text images via L0 regularized intensity and gradient prior." In CVPR,2014, pp. 2901-2908.
23 L. Xu, C. Lu, Y. Xu and J. Jia. "Image smoothing via l0 gradient minimization."ACM Transactions on Graphics (SIGGRAPH ASIA),vol. 30(6), 2011.
24 F. Jin, P. Fieguth, L. Winger and E. Jernigan. "Adaptive Wiener filtering of noisy images and image sequences." International Conference for Image Processing, IEEE, 2003, pp. 349352.
Miss Aparna Ashok
Mar Baselios College of Engineering - India
Mr. Deepa P. L.
Mar Baselios College of Engineering - India