By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

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:

Industrial Technology, 2005. ICIT 2005. IEEE International Conference on

Date of Conference:

14-17 Dec. 2005