Noise Reduction Using Wavelet with Application to Medical X-ray Image
Ling Wang, Jianming Lu, Yeqiu Li, Takashi Yahagi, Takahide Okamoto · 2006
When 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 practical problems such as medical X-ray image, astronomical and low-light image, the recorded data are not modeled by Gaussian noise but as the realization of a Possion process. In this paper, we propose a new approach to remove Poisson noise from medical X-ray image in the wavelet domain. This method improves the conventional BayesShrink approach based on wavelet coefficients characteristics of medical X-ray image. 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 median filter (DAMF). The proposed method shows more excellent results in amount of simulations of image denoising than the conventional methods