Title :
Patch-based locally optimal denoising
Author :
Chatterjee, Priyam ; Milanfar, Peyman
Author_Institution :
Dept. of Electr. Eng., Univ. of California, Santa Cruz, CA, USA
Abstract :
In our previous work [1], we formulated the fundamental limits of image denoising. In this paper, we propose a practical algorithm where the motivation is to realize a locally optimal denoising filter that achieves the lower bound. The proposed method is a patch-based Wiener filter that takes advantage of both geometrically and photometrically similar patches. The resultant approach has a nice statistical foundation while producing denoising results that are comparable to or exceeding the current state-of-the-art, both visually and quantitatively.
Keywords :
Wiener filters; image denoising; statistical analysis; geometrically similar patches; locally optimal denoising filter; motivation; patch-based Wiener filter; patch-based locally optimal denoising; photometrically similar patches; practical algorithm; statistical foundation; Image denoising; Nickel; Noise reduction; PSNR; Vectors; Image denoising; LMMSE estimator; Wiener filter; denoising bounds; image clustering;
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2011.6116184