Blind Deblurring with a Coupled Adaptive Sparse Prior

                                    Haichao Zhang           David Wipf            Yanning Zhang

                          Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013
                                        
Abstract
This paper presents a robust algorithm for estimating a single latent sharp image given either a single or multiple blurry and/or noisy observations.  The underlying multi-image blind deconvolution problem is solved by linking all of the observations together via a Bayesian-inspired penalty function which couples the unknown latent image, blur kernels, and noise levels together in a unique way.  This coupled penalty function enjoys a number of desirable properties, including a mechanism whereby the relative-concavity or shape is adapted as a function of the intrinsic quality of each blurry observation.  In this way, higher quality observations may automatically contribute more to the final estimate than heavily degraded ones.  The resulting algorithm, which requires no essential tuning parameters, can recover a high quality image from a set of observations containing potentially both blurry and noisy examples, without knowing a priori the degradation type of each observation.  Experimental results on both synthetic and real-world test images clearly demonstrate the efficacy of the proposed method.

Keywords
sparse blind deblurring, sparse blind deconvolution, coupled adaptive sparse prior, sparse estimation, non-convex optimization



Coupled Adaptive Sparse Penalty
Coupled: The penalty function is coupled over the latent sharp image, blur kernel and noise level, which is a crucial difference to the conventional separate penalties in the MAP approach.
Adaptive: Because of the coupling, the shape (sparsity prompting ability) of the penalty function is adjusted adaptively according to the estimated noise level and blur kernel, inducing an intrinsic mechanism of 'coarse-to-fine' estimation.
https://sites.google.com/site/hczhang1/projects/sparse-blind-deblurring/p_surf_30_.gif?attredirects=0
           (click to see the animation)


Results compared with state-of-the-art methods

   A. Dual Motion Deblurring: restoration with two blurry images


   

                                                       



   B. Dual Exposure Deblurring: restoration with blurry/noisy image pair



     


   C. Single Image Blind Deblurring

                                    Blurry Input                                                       Deblurred Image                                  Estimated Blur Kernel           
                    



 Deblur Demo

Sparse Blind Deblurring

                         


Related Publication and Software

Haichao Zhang, David Wipf and Yanning Zhang,  Multi-Image Blind Deblurring Using a Coupled Adaptive Sparse Prior, CVPR 2013

Haichao Zhang, David Wipf and Yanning Zhang,  Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior, accepted to TPAMI, 2013

David Wipf and Haichao Zhang, Revisiting Bayesian Blind Deconvolution, MSRA Tech. Report, 2013

H.Zhang, J. Yang, Y. Zhang, N Nasrabadi, T. Huang, Close the Loop: Joint Blind Image Restoration and Recognition with Sparse Representation Prior, ICCV 2011





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