Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

  • Authors:

  • M. Elad;M. Aharon

  • Affiliations:

  • Dept. of Comput. Sci., Technion- Israel Inst. of Technol., Haifa;-

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2006

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Abstract

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