IEEE Trans Image Process - Robust image deblurring with an inaccurate blur kernel.

Tópicos

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Resumo

Most existing nonblind image deblurring methods assume that the blur kernel is free of error. However, it is often unavoidable in practice that the input blur kernel is erroneous to some extent. Sometimes, the error could be severe, e.g., for images degraded by nonuniform motion blurring. When an inaccurate blur kernel is used as the input, significant distortions will appear in the image recovered by existing methods. In this paper, we present a novel convex minimization model that explicitly takes account of error in the blur kernel. The resulting minimization problem can be efficiently solved by the so-called accelerated proximal gradient method. In addition, a new boundary extension scheme is incorporated in the proposed model to further improve the results. The experiments on both synthesized and real images showed the efficiency and robustness of our algorithm to both the image noise and the model error in the blur kernel.

Resumo Limpo

exist nonblind imag deblur method assum blur kernel free error howev often unavoid practic input blur kernel erron extent sometim error sever eg imag degrad nonuniform motion blur inaccur blur kernel use input signific distort will appear imag recov exist method paper present novel convex minim model explicit take account error blur kernel result minim problem can effici solv socal acceler proxim gradient method addit new boundari extens scheme incorpor propos model improv result experi synthes real imag show effici robust algorithm imag nois model error blur kernel

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