Blur Kernel Optimization: A New Approach to Patch Selection with Adaptive Kernel Estimation

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Recently, many effective approaches appeared in the field of blind image deconvolution to reduce the computational cost. Using multiple smaller regions instead of whole image not only make the restoration efficient but also improves the results by discarding the ineffectual regions. It is observed that a study is needed to compare different methods for the selection of useful image patches and different schemes to utilize their blur kernels, which is aimed in the present work. A new patch selection method using Contrast based blur invariant features (CBIF) is proposed to find the useful regions which gives better results compared with others e.g., speed-up robust features (SURF), local binary patterns (LBP), local phase quantization (LPQ), maximally stable extremal regions (MSER), Canny and Sobel. In addition, gradually increasing contrast stretched levels shown to give better results compared with commonly used multiscale framework to avoid false local minima. It is also proposed that blur metric by Crete applied on latent image can be used for the selection of better kernel. It is observed that an effective strategy can give good results even when the patches are not selected carefully. The best results are obtained when our proposed patch selection is used with our “selective kernels averaging” scheme.

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Periodical:

Edited by:

Adrian Olaru

Pages:

531-538

DOI:

10.4028/www.scientific.net/AMM.436.531

Citation:

S. Yousaf and S. Y. Qin, "Blur Kernel Optimization: A New Approach to Patch Selection with Adaptive Kernel Estimation", Applied Mechanics and Materials, Vol. 436, pp. 531-538, 2013

Online since:

October 2013

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$38.00

[1] P. Campisi, and K. Egiazarian, Blind image deconvolution: theory and applications, (2007).

[2] Y. -L. You, and M. Kaveh, Blind image restoration by anisotropic regularization, Image Processing, IEEE Transactions on, vol. 8, no. 3, pp.396-407, (1999).

DOI: 10.1109/83.748894

[3] T. F. Chan, and C. K. Wong, Total variation blind deconvolution, IEEE Trans Image Process, vol. 7, no. 3, pp.370-5, (1998).

[4] J. N. Caron, N. M. Namazi, and C. J. Rollins, Noniterative blind data restoration by use of an extracted filter function, Applied Optics, vol. 41, no. 32, pp.6884-6889, (2002).

DOI: 10.1364/ao.41.006884

[5] R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, Removing camera shake from a single photograph., pp.787-794.

DOI: 10.1145/1179352.1141956

[6] Q. Shan, J. Jia, and A. Agarwala, High-quality motion deblurring from a single image, ACM Transactions on Graphics, vol. 27, no. 3, p.1, (2008).

DOI: 10.1145/1360612.1360672

[7] J. F. Cai, H. Ji, C. Liu, and Z. Shen, Blind motion deblurring from a single image using sparse approximation., pp.104-111.

[8] A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, Understanding and evaluating blind deconvolution algorithms., p.1964-(1971).

[9] D. Krishnan, T. Tay, and R. Fergus, Blind deconvolution using a normalized sparsity measure..

DOI: 10.1109/cvpr.2011.5995521

[10] P. Shearer, A. C. Gilbert, and A. O. Hero III, Correcting Camera Shake by Incremental Sparse Approximation, arXiv preprint arXiv: 1302. 0439, (2013).

DOI: 10.1109/icip.2013.6738118

[11] L. Yuan, J. Sun, L. Quan, and H. Y. Shum, Image deblurring with blurred/noisy image pairs..

DOI: 10.1145/1275808.1276379

[12] L. Xu, and J. Jia, Two-phase kernel estimation for robust motion deblurring, Computer Vision–ECCV 2010, pp.157-170, (2010).

DOI: 10.1007/978-3-642-15549-9_12

[13] J. Jia, Single image motion deblurring using transparency., pp.1-8.

[14] N. Joshi, R. Szeliski, and D. J. Kriegman, PSF estimation using sharp edge prediction..

[15] S. Cho, and S. Lee, Fast motion deblurring., p.145.

[16] J. Pan, R. Liu, Z. Su, and X. Gu, Kernel Estimation from Salient Structure for Robust Motion Deblurring, arXiv preprint arXiv: 1212. 1073, (2012).

[17] P. -H. Huang, Y. -M. Lin, H. -L. Yang, and S. -H. Lai, Image deblurring by exploiting inherent bi-level regions., pp.1321-1324.

[18] Z. Hu, and M. H. Yang, Good Regions to Deblur, European Vision on Computer Vision (ECCV 2012), (2012).

DOI: 10.1007/978-3-642-33715-4_5

[19] H. Bae, C. C. Fowlkes, and P. H. Chou, Patch Mosaic for Fast Motion Deblurring, The 11th Asian Conference on Computer Vision (ACCV), (2012).

DOI: 10.1007/978-3-642-37431-9_25

[20] C. C. F. Hyeoungho Bae, Pai H. Chou, Fast Motion Deblurring, ICCP12 Poster, (2012).

[21] S. Yousaf, and S. Qin, Blur Kernel Optimization with Contrast Levels and Effectual Patch Selection using SURF Features, International Conference on Imaging Systems and Techniques (in press), (2013).

DOI: 10.1109/ist.2013.6729717

[22] S. Yousaf, and S. Qin, Approach to Metric and Discrimination of Blur Based on Its Invariant Features, International Conference on Imaging Systems and Techniques (in press) (2013).

DOI: 10.1109/ist.2013.6729705

[23] F. Crete, T. Dolmiere, P. Ladret, and M. Nicolas, The blur effect: perception and estimation with a new no-reference perceptual blur metric., pp. 64920I-64920I-11.

DOI: 10.1117/12.702790

[24] M. E. Lopes, Estimating unknown sparsity in compressed sensing, arXiv preprint arXiv: 1204. 4227, (2012).

[25] S. D. Babacan, R. Molina, M. N. Do, and A. K. Katsaggelos, Bayesian blind deconvolution with general sparse image priors, Computer Vision–ECCV 2012, pp.341-355: Springer, (2012).

DOI: 10.1007/978-3-642-33783-3_25

[26] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, Speeded-up robust features (SURF), Computer Vision and Image Understanding, vol. 110, no. 3, pp.346-359, (2008).

DOI: 10.1016/j.cviu.2007.09.014

[27] J. Heikkila, and V. Ojansivu, Methods for local phase quantization in blur-insensitive image analysis., pp.104-111.

DOI: 10.1109/lnla.2009.5278397

[28] T. Ojala, M. Pietikainen, and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, no. 7, pp.971-987, (2002).

DOI: 10.1109/tpami.2002.1017623

[29] D. Nistér, and H. Stewénius, Linear time maximally stable extremal regions, ECCV (2008).

DOI: 10.1007/978-3-540-88688-4_14

[30] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB: Pearson Education India, (2004).

[31] H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik, LIVE image quality assessment database release 2, (2005).

[32] E. C. Larson, and D. M. Chandler, Most apparent distortion: full-reference image quality assessment and the role of strategy, Journal of Electronic Imaging, vol. 19, no. 1, (2010).

DOI: 10.1117/1.3267105

[33] A. G. Weber, "The USC-SIPI Image Database: Version 5, Original release: October (1997).

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