A Similar K-SVD Optimization Algorithm Generalizing the K-Means and the Bayesian tracking

DOI Open Access

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The last decade have seen tremendous improvement in the development of new image information processing and computational tools based on sparse representation. Today, in the information sciences, computer vision and image processing, the development of sparse representation algorithms led to convenient tools to transient compressed image (data) rapidly, to remove noise from image, and to get the super-resolution image. In the study of sparse representation of images, overcomplete dictionary is used. It contains prototype imageatoms. In this way, the images are described by sparse linear combinations of theses atoms. In this field has concentrated mainly on the design of a better dictionary. The generalized KMeans algorithm (K-SVD) [1] taught us a very good case. This paper has proposed an optimization algorithm adopting the Bayesian tracking and K-SVD analysis method. We analyze this algorithm and demonstrate its results on image data. Keywords-Sparese Repressentation; Bayesina Prior; K-SVD; Atom decomposition, Dictionaty.

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