[1]WANG Kejun,YANG Xiaofei.The contourlet denoising algorithm based on modified noise variance estimation[J].CAAI Transactions on Intelligent Systems,2015,10(2):255-260.[doi:10.3969/j.issn.1673-4785.201511015]
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The contourlet denoising algorithm based on modified noise variance estimation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 255-260 Column: 学术论文—人工智能基础 Public date: 2015-04-25
Title:
The contourlet denoising algorithm based on modified noise variance estimation
Author(s):
WANG Kejun;  YANG Xiaofei
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
image denoising; contourlet; standard deviation estimation of image noise; image structural characteristic measurement; histogram methods
CLC:
TP18;TN911.73
DOI:
10.3969/j.issn.1673-4785.201511015
Abstract:
In this paper, an algorithm based on the modified noise standard deviation estimation was proposed. The proposed algorithm provides a new approach for the foundation of common contourlet denoising methods. The combination of modified image structural characteristic measurement analysis method and filter method is used in selecting the image sub-block, which is suitable for computing standard deviation of noise. Finally, the histograms of those sub-images are used to estimate standard deviation of the image noise, which is subsequently used for denoising of contourlet. In the contrast experiment of standard deviation estimation the filter methods, partition methods and improved partition methods are chosen to compare with the standard deviation estimation algorithm. In the contrast experiment of denoising, the universal wavelet threshold denoising, the common contourlet threshold denoising extended from wavelet threshold, the contourlet denoising based on wiener filtering, and the contourlet denoising based on coefficient models are chosen to compare with the denoising algorithm proposed in this paper. The experimental results showed that the method can estimate standard deviation of images more accurately and performs more reliable than other contourlet denoising algorithms.
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