27 February 2015 Blind deconvolution of images with model discrepancies using maximum a posteriori estimation with heavy-tailed priors
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Abstract
Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straight- forward maximum a posteriori estimation incorporating sparse priors and mechanism to deal with boundary artifacts, combined with an efficient numerical method can produce results which compete with or outperform much more complicated state-of-the-art methods. Our method is naturally extended to deal with overexposure in low-light photography, where linear blurring model is violated.
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Jan Kotera, Filip Šroubek, "Blind deconvolution of images with model discrepancies using maximum a posteriori estimation with heavy-tailed priors", Proc. SPIE 9404, Digital Photography XI, 94040B (27 February 2015); doi: 10.1117/12.2077158; https://doi.org/10.1117/12.2077158
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