Removal of Gaussian Noise from Degraded Images in Wavelet Domain

摘要

Observed images arc 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.