Convergence Analysis of MAP based Blur Kernel Estimation

ICCV 2017 Sunghyun ChoSeungyong Lee

One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown to prefer blurry images to sharp natural images... (read more)

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