A new algorithm for image denoising based on tetrolet transform

Cai-lian Li; Ji-xiang Sun; Yao-hong Kang

doi:10.1117/12.866702

19 August 2010 A new algorithm for image denoising based on tetrolet transform

Cai-lian Li, Ji-xiang Sun, Yao-hong Kang

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 78201L (2010) https://doi.org/10.1117/12.866702
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

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.

Citation Download Citation

Cai-lian Li, Ji-xiang Sun, and Yao-hong Kang "A new algorithm for image denoising based on tetrolet transform", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201L (19 August 2010); https://doi.org/10.1117/12.866702

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available