Training dictionary by granular computing with L∞-norm for patch granule–based image denoising

Considering the objects by different granularity reflects the recognition common law of people, granular computing embodies the transformation between different granularity spaces. We present the image denoising algorithm by using the dictionary trained by granular computing with L∞-norm, which real...

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Bibliographic Details
Published inJournal of algorithms & computational technology Vol. 12; no. 2; pp. 136 - 146
Main Authors Liu, Hongbing, Liu, Gengyi, Ma, Xuewen, Liu, Daohua
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 01.06.2018
Sage Publications Ltd
SAGE Publishing
Subjects
Algorithms
Computation
Dictionaries
Granular materials
Noise measurement
Noise reduction
Object recognition
Signal to noise ratio
Transformations
image patch
L∞-norm
Granular computing
image denoising
patch granule
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Summary:Considering the objects by different granularity reflects the recognition common law of people, granular computing embodies the transformation between different granularity spaces. We present the image denoising algorithm by using the dictionary trained by granular computing with L∞-norm, which realizes three transformations, (1) the transformation from image space to patch granule space, (2) the transformation between granule spaces with different granularities, and (3) the transformation from patch granule space to image space. We demonstrate that the granular computing with L∞-norm achieved the comparable peak signal to noise ratio (PSNR) measure compared with BM3D and patch group prior based denoising for eight natural images.
ISSN:1748-3026
1748-3018
1748-3026
DOI:10.1177/1748301818761131