Image denoising using multiple compaction domains

Author

Ishwar, Prakash ; Ratakonda, Krishna ; Moulin, Pierre ; Ahuja, Narendra

Author_Institution

Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL, USA

Volume

3

fYear

1998

fDate

12-15 May 1998

Firstpage

1889

Abstract

We present a novel framework for denoising signals from their compact representation in multiple domains. Each domain captures, uniquely, certain signal characteristics better than others. We define confidence sets around data in each domain and find sparse estimates that lie in the intersection of these sets, using a POCS algorithm. Simulations demonstrate the superior nature of the reconstruction (both in terms of mean-square error and perceptual quality) in comparison to the adaptive Wiener filter

Keywords

Gaussian noise; image reconstruction; image representation; wavelet transforms; white noise; AWGN; POCS algorithm; adaptive Wiener filter; compact representation; confidence sets; image denoising; image reconstruction; mean-square error; multiple compaction domains; multiple signal representation; perceptual quality; signal characteristics; signal denoising; simulations; sparse estimates; wavelet filters; AWGN; Additive white noise; Compaction; Gaussian noise; Image denoising; Image reconstruction; Noise reduction; Signal representations; Wavelet domain; Wiener filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681833

Filename

681833