Blind image deconvolution using the Gaussian scale mixture fields of experts prior

In this paper, a blind image deconvolution method which is derived from Bayesian probabilistic framework is proposed. A robust prior named Gaussian Scale Mixture Fields of Experts (GSM FoE) and a prior that is constructed with the lp-norm (p ≈ 1.5) are adopted to regularize the latent image and the point spread function (PSF) respectively. We use a two phase optimization approach to solve the resulted maximum a-posteriori (MAP) estimation problem, and a simple gradient selecting method is incorporated into the alternating minimization to improve the accuracy of the estimated PSF. Experiments on both synthetic and real world blurred images show that our method can achieve results with high quality.

[1]  J. P. Lewis,et al.  Fast Template Matching , 2009 .

[2]  Li Xu,et al.  Unnatural L0 Sparse Representation for Natural Image Deblurring , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Qi Li,et al.  Blind image deconvolution using the Fields of Experts prior , 2012 .

[4]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Michael Elad,et al.  Bi-l0-l2-norm regularization for blind motion deblurring , 2014, J. Vis. Commun. Image Represent..

[6]  Qi Li,et al.  A piecewise local regularized Richardson–Lucy algorithm for remote sensing image deconvolution , 2011 .

[7]  Sunghyun Cho,et al.  Fast motion deblurring , 2009, SIGGRAPH 2009.

[8]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[9]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[11]  Junbin Gao,et al.  Blind image deblurring via coupled sparse representation , 2014, J. Vis. Commun. Image Represent..

[12]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[13]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[14]  Nian Peng,et al.  Concave-convex norm ratio prior based double model and fast algorithm for blind deconvolution , 2016, Neurocomputing.

[15]  Luís B. Almeida,et al.  Blind and Semi-Blind Deblurring of Natural Images , 2010, IEEE Transactions on Image Processing.

[16]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[17]  William T. Freeman,et al.  What makes a good model of natural images? , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

[19]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, CVPR.

[20]  A. Tikhonov,et al.  Numerical Methods for the Solution of Ill-Posed Problems , 1995 .

[21]  Michel Barlaud,et al.  Deterministic edge-preserving regularization in computed imaging , 1997, IEEE Trans. Image Process..

[22]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Blind Deconvolution Using a Total Variation Prior , 2009, IEEE Transactions on Image Processing.

[23]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[24]  Qi Li,et al.  Multi-frame blind deconvolution using sparse priors , 2012 .

[25]  Edmund Y Lam,et al.  Maximum a posteriori blind image deconvolution with Huber-Markov random-field regularization. , 2009, Optics letters.

[26]  Donald Geman,et al.  Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..

[27]  Michael J. Black,et al.  Fields of Experts , 2009, International Journal of Computer Vision.

[28]  William T. Freeman,et al.  Removing camera shake from a single photograph , 2006, SIGGRAPH 2006.

[29]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[30]  Rob Fergus,et al.  Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.