Sparse representation of natural image based on Contourlet overcomplete dictionary

DOI

10.2991/iiicec-15.2015.290How to use a DOI?

Keywords

Overcomplete Dictionary; Contourlet Basis; Nonlinear Approximation

Abstract

In this paper, textural features of natural image are researched from the perspective of highly nonlinear approximation theory, in view of the characteristic that a contour of natural image is composed of piecewise regular geometrical curves of the image plane. According to nonlinear approximation theory and multi-scale geometric analysis method, a dictionary based on Contourlet basis function is also proposed in this paper. This dictionary approximates texture area of image by using Orthogonal Matching Pursuit (OMP) method. Experimental results show that Peak Signal to Noise Ratio (PSNR) and Sparsity Ratio (SR) of nonlinear approximated images can be improved effectively by using the proposed dictionary.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhengfang Deng
AU  - Jing Jin
AU  - Jinshan Su
AU  - Xingyu Yang
PY  - 2015/03
DA  - 2015/03
TI  - Sparse representation of natural image based on Contourlet overcomplete dictionary
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 1313
EP  - 1318
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.290
DO  - 10.2991/iiicec-15.2015.290
ID  - Deng2015/03
ER  -