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Noise reduction using wavelet with application to medical X-ray image

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5 Author(s)
Ling Wang ; Graduate Sch. of Sci. & Technol., Chiba Univ., Japan ; Jianming Lu ; Yeqiu Li ; T. Yahagi
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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

Published in:

2005 IEEE International Conference on Industrial Technology

Date of Conference:

14-17 Dec. 2005