DocumentCode :
2312684
Title :
Adaptive Wiener filtering of noisy images and image sequences
Author :
Jin, F. ; Fieguth, P. ; Winger, L. ; Jernigan, E.
Author_Institution :
Dept. of Syst. Design Eng., Waterloo Univ., Ont., Canada
Volume :
3
fYear :
2003
fDate :
14-17 Sept. 2003
Abstract :
In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.
Keywords :
Wiener filters; adaptive filters; image denoising; image sequences; statistical analysis; wavelet transforms; adaptive Wiener filtering; adaptive weighted averaging; image sequences; noisy images; peak-to-peak SNR; second-order statistics; wavelet domain; Adaptive filters; Additive noise; Gaussian noise; Image denoising; Image sequences; Noise reduction; PSNR; Statistics; Wavelet domain; Wiener filter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-7750-8
Type :
conf
DOI :
10.1109/ICIP.2003.1247253
Filename :
1247253
Link To Document :
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