MRF-Based Blind Image Deconvolution

Nikos Komodakis, Nikos Paragios

Abstract: This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.