Blind Deconvolution Using A Normalized Sparsity Measure

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Blind Deconvolution Using A Normalized Sparsity Measure
Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of workaround methods are needed to yield good results (e.g. Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.
Dilip Krishnan, Rob Fergus
Added 09 May 2011
Updated 09 May 2011
Type Journal
Year 2011
Where CVPR
Authors Dilip Krishnan, Rob Fergus
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