A new method to remove the Gaussian noise from image in wavelet domain

Summary form only given. In experiments, the observed image is often modeled as a noisy image. If the image is embedded in an additive Gaussian noise, the classical solution to the noise removal problem is to use the Wiener filter or median filter. In recent years, the BayesShrinkWavelet method has received attention. In this paper, we present a method to remove the noise from an image including substantial Gaussian noise using scaling coefficients and DACWMF (directional adaptive center weighted median filter) procedure which is based on the BayesShrink method. In this way, we can filter the large-amplitude noise which cannot be removed using BayesShrink and improve the quality of the "cleaned" image.