Edge-based blur kernel estimation using patch priors
Edge-based blur kernel estimation using patch priors
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DOI：
10.1109/iccphot.2013.6528301
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Libin Sun;Sunghyun Cho;Jue Wang;James Hays
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文献类型：
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作者：
Libin Sun;Sunghyun Cho;Jue Wang;James Hays
盲图像去卷积，即，从输入模糊图像y估计模糊核k和潜像x是严重不适定的问题。本文介绍了一种新的基于块的盲反卷积核估计策略。我们的方法估计一个“可信”的子集，通过施加补丁之前专门定制的图像边缘和角落的图元的外观建模。为了选择合适的补丁先验，我们检查了从自然图像数据集学习的统计先验和从合成结构学习的简单补丁先验。基于补丁先验，我们迭代地恢复部分潜像x和模糊核k。一个全面的评价表明，我们的方法实现了国家的最先进的结果均匀模糊的图像。
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.