Training dictionary by granular computing with L∞-norm for patch granule–based image denoising
| Autor: | Gengyi Liu, Hongbing Liu, Xuewen Ma, Daohua Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Noise reduction lcsh:T57-57.97 lcsh:Mathematics Granular computing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications Pattern recognition 02 engineering and technology lcsh:QA1-939 Peak signal-to-noise ratio Condensed Matter::Soft Condensed Matter Norm (mathematics) Computer Science::Computer Vision and Pattern Recognition lcsh:Applied mathematics. Quantitative methods 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Granularity Artificial intelligence Image denoising business |
| Zdroj: | Journal of Algorithms & Computational Technology, Vol 12 (2018) |
| ISSN: | 1748-3026 1748-3018 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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