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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

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2 Author(s)
Elad, M. ; Dept. of Comput. Sci., Technion- Israel Inst. of Technol., Haifa ; Aharon, M.

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods

Published in:

Image Processing, IEEE Transactions on  (Volume:15 ,  Issue: 12 )

Date of Publication:

Dec. 2006

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