Removing Poisson noise from images in wavelet domain

In experiments, observations are often modelled as a noisy signal. If the signal is embedded in an additive Gaussian noise, its estimation is often done by finding a wavelet basis that concentrates the signal energy over few coefficients and by thresholding the noisy coefficients. However, in many problems of physics, the recorded data are not modelled by Gaussian noise but as the realization of a Poisson process. In this paper, we study a new approach of removing Poisson noise from a degraded image in wavelet domain. This method widens the conventional BayesShrink approach and is operated by processing not only detail coefficients (wavelet coefficients) but also the coefficients related to the rough approximation (approximation coefficients). In order to remove the large-amplitude noise which cannot be removed by conventional wavelet shrink methods, we propose a new type of directional adaptive center weighted median filter (DACWMF)