DocumentCode :
3449427
Title :
Reconstruction of Ridgelet Coefficients Using Total Variation Minimization
Author :
Chengzhi, Deng ; Hanqiang, Cao ; Shengqian, Wang
Author_Institution :
Huazhong Univ. of Sci. & Technol., Wuhan
fYear :
2007
fDate :
23-25 May 2007
Firstpage :
2411
Lastpage :
2414
Abstract :
The combination of ordinary wavelet shrinkage with total variation minimization was successfully applied. In this paper, we apply the technique with respect to ridgelet coefficients. Firstly, a translation-invariant ridgelet transform is proposed. And then, an image denoising algorithm, based on ridgelet shrinkage and total variation minimization, is given. This algorithm preserves the important information of image and reduces the noise by thresholding small ridgelet coefficients. By replacing these thresholded coefficients by values minimizing the total variation, the algorithm reduces the pseudo-Gibbs artifacts. Experiment results show that this algorithm yields significantly superior image quality and higher peak signal to noise ratio (PSNR).
Keywords :
image denoising; image reconstruction; wavelet transforms; PSNR; image denoising algorithm; peak signal to noise ratio; pseudo-Gibbs artifacts; ridgelet coefficients reconstruction; total variation minimization; translation-invariant ridgelet transform; wavelet shrinkage; Gaussian noise; Industrial electronics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications, 2007. ICIEA 2007. 2nd IEEE Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-0737-8
Electronic_ISBN :
978-1-4244-0737-8
Type :
conf
DOI :
10.1109/ICIEA.2007.4318843
Filename :
4318843
Link To Document :
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