Image decomposition via learning the morphological diversity

Authors:
Yafeng Li;Xiangchu Feng
Affiliations:
Department of Applied Mathematics, Xidian University, Xi An 710071, China and Department of Computer Science, Baoji University of Arts and Science, Baoji 721007, China;Department of Applied Mathematics, Xidian University, Xi An 710071, China
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

Image decomposition aims to separate different features in images. Based on dictionary learning (DL) techniques, this letter discusses two new algorithms for image decomposing into a linear combination of morphological components. The proposed algorithms can be viewed as the extensions of DL-based image denoising algorithm. Experiments show that the learned dictionaries by the proposed algorithms can describe the different components of image effectively and leads to high quality image decomposition performance.