Optimized Wavelet Denoising Algorithm Using Hybrid Noise Model for Radiographic Images

To improve the performance of denoising algorithm for industrial radiographic testing (RT) images, an optimized wavelet denoising algorithm using hybrid noise model (WDHM) is proposed. Firstly, a hybrid noise model is constructed by analyzing the noise components to solve the problem of noise varian...

Full description

Saved in:
Bibliographic Details
Published in2014 Seventh International Symposium on Computational Intelligence and Design Vol. 1; pp. 144 - 149
Main Authors Changying Dang, Jianmin Gao, Zhao Wang, Fumin Chen, Yulin Xiao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
edge preserving
Filtering
fixed point median
hybrid noise model
Noise
radiographic image
wavelet denoising
Wavelet transforms
Wiener filters
Online AccessGet full text

Cover

More Information
Summary:To improve the performance of denoising algorithm for industrial radiographic testing (RT) images, an optimized wavelet denoising algorithm using hybrid noise model (WDHM) is proposed. Firstly, a hybrid noise model is constructed by analyzing the noise components to solve the problem of noise variance estimation when wavelet denoising is adopted for RT images. Then, a wavelet processing threshold is determined by the hybrid noise model, and noise in RT images is reduced by wavelet denoising. Meanwhile, a fixed-point median processing is used for eliminating image distortion caused by wavelet denoising. Comparing with conventional wavelet and Wiener filter denoising, the experimental results show that WDHM not only gets good denoising effectiveness, but also keeps a good balance between removing noise and preserving edge characteristics well.
ISBN:9781479970049
1479970042
DOI:10.1109/ISCID.2014.88