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Image denoising with patch estimation and low patch-rank regularization
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DOI：10.1007/s11042-013-1535-4.png)
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Abstract
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In this paper, we propose an image denoising algorithm for one special class of images which have periodical textures and contaminated by poisson noise using patch estimation and low patch-rank regularization. In order to form the data fidelity term, we take the patch-based poisson likelihood, which will effectively remove the 'blurring' effect. For the sparse prior, we use the low patch-rank as the regularization, avoiding the choosing of dictionary. Putting together the data fidelity and the prior terms, the denoising problem is formulated as the minimization of a maximum likehood objective functional involving three terms: the data fidelity term; a sparsity prior term, in the form of the low patch-rank regularization ;and a non-negativity constraint (as Poisson data are positive by definition). Experimental results show that the new method performs well for this special class of images which have periodical texture, and even for images with not strictly periodical textures.
Keywords:
Patch estimation
Low patch-rank
Proximal splitting method
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