DocumentCode :
2670364
Title :
Study on the image de-noising algorithm of adaptive threshold based on wavelet transform in the unclear radiation environment
Author :
Lin, Mao-song ; Xie, Gang
Author_Institution :
Sch. of Inf. Eng., Southwest Univ. of Sci. & Technol., Mianyang, China
fYear :
2012
fDate :
23-25 May 2012
Firstpage :
1944
Lastpage :
1948
Abstract :
In order to get a better image in the nuclear radiation environment, this paper presents an adaptive threshold image de-nosing algorithm based on the concept of wavelet multi-resolution. First of all, remove the big isolated points by median filtering in the image; achieve the multi-resolution analysis by lifting wavelet transform; then obtain the threshold value of the wavelet coefficients by the Bayesian Shrink adaptive algorithm; finally recover the image by the inverse lifting wavelet transform. As a result of multi-resolution adaptive threshold method, the non-stationary characteristics of the signal can be described quite well, and remove the noise according to the distribution of the signal and noise at different resolutions. The experimental results show that this method can get a better de-nosing effect in the nuclear radiation environment.
Keywords :
Bayes methods; filtering theory; image denoising; image segmentation; median filters; wavelet transforms; Bayesian shrink adaptive algorithm; adaptive threshold image denosing algorithm; inverse lifting wavelet transform; median filtering; multiresolution adaptive threshold method; multiresolution analysis; nuclear radiation environment; unclear radiation environment; wavelet coefficients; Bayesian methods; Filtering; Image edge detection; Noise; Wavelet coefficients; lifting wavelet transform; median filtering; wavelet de-nosing; wavelet threshold;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
Type :
conf
DOI :
10.1109/CCDC.2012.6244313
Filename :
6244313
Link To Document :
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