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arXiv · 1311.4029

Blind Deconvolution with Non-local Sparsity Reweighting

Dilip Krishnan·Joan Bruna·Rob Fergus
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Abstract

Blind deconvolution has made significant progress in the past decade. Most successful algorithms are classified either as Variational or Maximum a-Posteriori ($MAP$). In spite of the superior theoretical justification of variational techniques, carefully constructed $MAP$ algorithms have proven equally effective in practice. In this paper, we show that all successful $MAP$ and variational algorithms share a common framework, relying on the following key principles: sparsity promotion in the gradient domain, $l_2$ regularization for kernel estimation, and the use of convex (often quadratic) cost functions. Our observations lead to a unified understanding of the principles required for successful blind deconvolution. We incorporate these principles into a novel algorithm that improves significantly upon the state of the art.

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Dilip Krishnan, Joan Bruna, Rob Fergus. 2013-11-16. Blind Deconvolution with Non-local Sparsity Reweighting. https://arxiv.org/abs/1311.4029

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