A modified image denoising algorithm by labeling and 3D wavelet transform

In order to sharpen image details and reducing noise, based on the multi-analysis wavelet threshold denoising method, a Labeling-based block-matching and wavelet transform filtering method combine hard and soft threshold denoising approaches (BWHS) is proposed in this paper. First, we estimate the n...

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Published in2010 International Conference on Computer Application and System Modeling (ICCASM 2010) Vol. 13; pp. V13-44 - V13-47
Main Authors Shunyong Zhou, Xingzho Xiong, Wenling Xie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2010
Subjects
Blockmatching
Filtering theory
Image Denoising
Noise
Noise reduction
Noise Variance
Sharpen Image
Wavelet coefficients
Wavelet Transform
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Summary:In order to sharpen image details and reducing noise, based on the multi-analysis wavelet threshold denoising method, a Labeling-based block-matching and wavelet transform filtering method combine hard and soft threshold denoising approaches (BWHS) is proposed in this paper. First, we estimate the noise variance of image. Second compute the matching blocks, and construct the 3D data array of those similar blocks, the high and low frequency sub-bands denoised by the best soft threshold, hard threshold that result from the iterative calculation of noise variance respectively, Finally, sharpen image details using DC coefficients of LL frequency sub-bands. Simulation results show that the algorithm can preserve and sharpen image details and effectively attenuate noise. Moreover, it has better performance than the traditional soft threshold, hard threshold, median and mean denoising methods.
ISBN:9781424472352
1424472350
ISSN:2161-9069
DOI:10.1109/ICCASM.2010.5622647