Color image denoising using wavelets and minimum cut analysis

Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images, especially when it exploits the interscale correlations of wavelet coefficients. Intrascale correlations can further improve the denoising performance, but the gain for grayscale images is gener...

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Bibliographic Details
Published in:IEEE signal processing letters 2005-11, Vol.12 (11), p.741-744
Main Authors: Nai-Xiang Lian, Zagorodnov, V., Yap-Peng Tan
Format: Article
Language:English
Subjects:
Chromium
Color difference
color image denoising
Colored noise
Gray-scale
Image analysis
Image color analysis
minimum cut
Noise reduction
Signal processing algorithms
Space technology
wavelet
Wavelet analysis
Wavelet coefficients
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Summary:Wavelet thresholding has proven to be an efficient edge-preserving denoising method for grayscale images, especially when it exploits the interscale correlations of wavelet coefficients. Intrascale correlations can further improve the denoising performance, but the gain for grayscale images is generally small. In this letter, we demonstrate that the gain can become substantial in color image denoising, especially for smooth image color-difference components. We then propose a new denoising method, based on the minimum cut algorithm, to exploit both the interscale and intrascale correlations of wavelet coefficients. The proposed method achieves up to 5-dB gain in peak signal-to-noise ratio for color-difference images and leads to fewer visual color artifacts.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2005.856865