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Proceedings Paper

An adaptive threshold method for image denoising based on wavelet domain
Author(s): Jiakun Xu; Kun Zhang; Mingyao Xu; Zhigang Zhou
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Paper Abstract

In this paper, a new thresholding function is proposed for image denoising in the wavelet domain. This function is used in an adaptive manner in a method that inspired form Thresholding Neural Network (TNN). Classic functions set the coefficients below the threshold value to zero, but in our proposed method these coefficients are tuned by a polynomial function. This tuning increases the capability of the function since we can attenuate the coefficients that are below the threshold value and close to it to a value less than the far coefficients. This function has some advantages over classical methods and produces better results in noise reduction. Besides the thresholding function, the subband-adaptive methods was adopted that the threshold value is selected differently for each detail subband. The simulation results show that the proposed thresholding function has superior performance compared to conventional methods when used with the proposed adaptive thresholding method. This makes it an efficient method in image denoising applications.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954M (30 October 2009); doi: 10.1117/12.831402
Show Author Affiliations
Jiakun Xu, Wuhan Univ. of Science and Engineering (China)
Huazhong Univ. of Science and Technology (China)
Kun Zhang, Huazhong Univ. of Science and Technology (China)
Mingyao Xu, Wuhan Univ. of Science and Engineering (China)
Zhigang Zhou, Wuhan Univ. of Science and Engineering (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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