Adaptive Wiener filtering of noisy images and image sequences

TitleAdaptive Wiener filtering of noisy images and image sequences
Publication TypeConference Paper
Year of Publication2003
AuthorsJin, F., P. Fieguth, L. Winger, and E. Jernigan
Conference NameIEEE International Conference on Image Processing
Conference LocationSpain
Keywordsadaptive filters, adaptive weighted averaging, adaptive Wiener filtering, Image Denoising, image sequences, noisy images, peak-to-peak SNR, second-order statistics, statistical analysis, wavelet domain, wavelet transforms, Wiener filters

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.