Blind image deconvolution using the Fields of Experts prior |
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Authors: | Wende DongHuajun Feng Zhihai XuQi Li |
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Affiliation: | State Key Laboratory of Modern Optical Instrumentation, No.38, Zheda road, West Lake district, Hangzhou city, Zhejiang province 310027, China |
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Abstract: | In this paper, we present a method for single image blind deconvolution. To improve its ill-posedness, we formulate the problem under Bayesian probabilistic framework and use a prior named Fields of Experts (FoE) which is learnt from natural images to regularize the latent image. Furthermore, due to the sparse distribution of the point spread function (PSF), we adopt a Student-t prior to regularize it. An improved alternating minimization (AM) approach is proposed to solve the resulted optimization problem. Experiments on both synthetic and real world blurred images show that the proposed method can achieve results of high quality. |
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Keywords: | Blind image deconvolution Fields of Experts (FoE) prior Student-t prior Alternating minimization (AM) approach |
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