Edge-based blur kernel estimation using patch priors

Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a "trusted" subset of...

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Bibliographic Details
Published in2013 IEEE International Conference on Computational Photography (ICCP) pp. 1 - 8
Main Authors Libin Sun, Sunghyun Cho, Jue Wang, Hays, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2013
Subjects
Deconvolution
Estimation
Image edge detection
Image restoration
Kernel
Noise
Optimization
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Summary:Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a "trusted" subset of x by imposing a patch prior specifically tailored towards modeling the appearance of image edge and corner primitives. To choose proper patch priors we examine both statistical priors learned from a natural image dataset and a simple patch prior from synthetic structures. Based on the patch priors, we iteratively recover the partial latent image x and the blur kernel k. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images.
ISBN:1467364630
9781467364638
DOI:10.1109/ICCPhot.2013.6528301