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Super-Resolution using Regularized Orthogonal Matching Pursuit based on compressed sensing theory in the wavelet domain

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1 Author(s)
Tingting Li ; College of Mathematics and Physics, Chongqing University, Chongqing, China 400044

We proposed a compressed sensing Super Resolution algorithm based on wavelet. The proposed algorithm performs well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software and state-of-the-art methods. Experiments demonstrate that, the proposed algorithm is competitive among contemporary Super Resolution methods.

Published in:

Computational Intelligence in Robotics and Automation (CIRA), 2009 IEEE International Symposium on

Date of Conference:

15-18 Dec. 2009