Abstract
Observed images are often corrupted by Gaussian noise. If the image is embedded in small-amplitude Gaussian noise, the noise can be removed by applying a Wiener filter. Recently, the BayesShrink wavelet method has attracted considerable attention as a denoising technique. In this paper, we propose a method for removal of Gaussian noise of large amplitude as well as of small amplitude which cannot be removed only by exploiting the BayesShrink wavelet method. Our approach is a combination of the BayesShrink wavelet method with the directional adaptive center-weighted median filter. Applying the proposed method to an image corrupted by large-amplitude Gaussian noise, a clean image can be obtained. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 11–18, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10029
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