Title :
Image Deblurring Regularized by Wavelet Probability Shrink
Author_Institution :
Sch. of Comput. & Commun. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Abstract :
An image deblurring algorithm based on wavelet probability shrink regularization is proposed. Denoise and deblur were alternatively executed by least square approximate and probability shrinkage. After several iterations of deblur, probability shrink based on stationary wavelet transform (SWT) were used once for denoising. Experimental results show that proposed algorithm obtained better results on several benchmark images than classical regularization techniques such as wavelet soft shrink, TV, or even non-local TV proposed more recently.
Keywords :
image restoration; least squares approximations; probability; wavelet transforms; image deblurring; least square approximation; probability shrinkage; stationary wavelet transform; wavelet probability shrink regularization; Image restoration; PSNR; TV; Wavelet domain; Wavelet transforms; Image Deblurring; ProbabiLity Shrink; Stationary Wavelet Transform (SWT); Wavelet Shrink;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
DOI :
10.1109/ICDMA.2011.152
