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      BM3D Frames and Variational Image Deblurring

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          Abstract

          A family of the Block Matching 3-D (BM3D) algorithms for various imaging problems has been recently proposed within the framework of nonlocal patch-wise image modeling [1], [2]. In this paper we construct analysis and synthesis frames, formalizing the BM3D image modeling and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem: one given by minimization of the single objective function and another based on the Nash equilibrium balance of two objective functions. The latter results in an algorithm where the denoising and deblurring operations are decoupled. The convergence of the developed algorithms is proved. Simulation experiments show that the decoupled algorithm derived from the Nash equilibrium formulation demonstrates the best numerical and visual results and shows superiority with respect to the state of the art in the field, confirming a valuable potential of BM3D-frames as an advanced image modeling tool.

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          Most cited references6

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          ForWaRD: Fourier-Wavelet Regularized Deconvolution for Ill-Conditioned Systems

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            From Local Kernel to Nonlocal Multiple-Model Image Denoising

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              Adaptive total variation image deblurring: A majorization–minimization approach

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                Author and article information

                Journal
                30 June 2011
                Article
                10.1109/TIP.2011.2176954
                1106.6180
                71048496-7a16-4f9a-8b38-df7c686f2a09

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Submitted to IEEE Transactions on Image Processing on May 18, 2011. implementation of the proposed algorithm is available as part of the BM3D package at http://www.cs.tut.fi/~foi/GCF-BM3D
                math.OC

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