BLIND IMAGE RESTORATION OF BLURRED IMAGES USING FAILING DETECTION PROCESS

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Image restoration, which restores a clear image from a single blurry image, is a difficult process of estimating two unknowns: a point-spread function (PSF) and an ideal image. In this paper, we propose a novel blind deconvolution method to alternately estimate PSF and the latent image. We apply a gradient reliability map that enables edge selection appropriate for PSF estimation and an energy function that enables estimation of convergence states. This method improves restoration performance by eliminating noise adversely affecting estimation. Additionally, a restoration failure detection process is added by using the evaluation function. Experimental results show that the robustness of the proposed method is improved.

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