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Proceedings Paper

A new algorithm for image denoising based on tetrolet transform
Author(s): Cai-lian Li; Ji-xiang Sun; Yao-hong Kang
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Paper Abstract

This paper introduces a new class of denoising function that has continuous derivative for image denoising. And a new algorithm are presented. First, we apply tetrolet transform to noise image and obtained tetrolet coefficient. Second, by using the new denoising function, we present an adaptive method based on SURE Risk. Instead of the global hard-thresholding algorithm for image denoising, we minimize an estimate of the mean square error by using adaptive genetic algorithm. At last Numerical experiments show that the proposed new algorithm can significantly outperform the original hard-thresholding method both in terms of PSNR and in visual quality.

Paper Details

Date Published: 19 August 2010
PDF: 7 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201L (19 August 2010); doi: 10.1117/12.866702
Show Author Affiliations
Cai-lian Li, National Univ. of Defense Technology (China)
Ji-xiang Sun, National Univ. of Defense Technology (China)
Yao-hong Kang, Hai-nan Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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