Image Noise Removal via Wavelet Transform and r/K-PSO
Image noise removal is a classic problem. In this paper, a novel scheme combining r/K-PSO and wavelet transform was introduced to removal image noise. By wavelet transform, image was decomposed into detail subbands and approximation subband. Every detail subband will be shrunk by a special threshold...
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| creator | Yunyi Yan Baolong Guo Zhanlong Yang Xiang Fu |
| description | Image noise removal is a classic problem. In this paper, a novel scheme combining r/K-PSO and wavelet transform was introduced to removal image noise. By wavelet transform, image was decomposed into detail subbands and approximation subband. Every detail subband will be shrunk by a special threshold instead of a universe one. A novel optimization algorithm named r/K-PSO was introduced to optimize and determine the thresholds. The main idea of r/K-PSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. And experimental results also proved that the proposed noise removal scheme optimized by r/K-PSO was superior to 2-D Winner Filtering (WF), universal hard-thresholding (UHT) and universal soft-thresholding (UST) in terms of peak signal-to-noise ratio (PSNR). |
| doi_str_mv | 10.1109/ICNC.2008.608 |
| format | Conference Proceeding |
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And experimental results also proved that the proposed noise removal scheme optimized by r/K-PSO was superior to 2-D Winner Filtering (WF), universal hard-thresholding (UHT) and universal soft-thresholding (UST) in terms of peak signal-to-noise ratio (PSNR).</description><identifier>ISSN: 2157-9555</identifier><identifier>ISBN: 9780769533049</identifier><identifier>ISBN: 0769533043</identifier><identifier>DOI: 10.1109/ICNC.2008.608</identifier><identifier>LCCN: 2008904182</identifier><language>eng</language><publisher>IEEE</publisher><subject>Environmental factors ; Filtering ; Frequency domain analysis ; Organisms ; Particle swarm optimization ; Productivity ; PSNR ; Wavelet domain ; Wavelet transforms ; Wiener filter</subject><ispartof>2008 Fourth International Conference on Natural Computation, 2008, Vol.5, p.544-548</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4667494$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4667494$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yunyi Yan</creatorcontrib><creatorcontrib>Baolong Guo</creatorcontrib><creatorcontrib>Zhanlong Yang</creatorcontrib><creatorcontrib>Xiang Fu</creatorcontrib><title>Image Noise Removal via Wavelet Transform and r/K-PSO</title><title>2008 Fourth International Conference on Natural Computation</title><addtitle>ICNC</addtitle><description>Image noise removal is a classic problem. 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In this paper, a novel scheme combining r/K-PSO and wavelet transform was introduced to removal image noise. By wavelet transform, image was decomposed into detail subbands and approximation subband. Every detail subband will be shrunk by a special threshold instead of a universe one. A novel optimization algorithm named r/K-PSO was introduced to optimize and determine the thresholds. The main idea of r/K-PSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. And experimental results also proved that the proposed noise removal scheme optimized by r/K-PSO was superior to 2-D Winner Filtering (WF), universal hard-thresholding (UHT) and universal soft-thresholding (UST) in terms of peak signal-to-noise ratio (PSNR).</abstract><pub>IEEE</pub><doi>10.1109/ICNC.2008.608</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2008 Fourth International Conference on Natural Computation, 2008, Vol.5, p.544-548 |
| issn | 2157-9555 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Environmental factors Filtering Frequency domain analysis Organisms Particle swarm optimization Productivity PSNR Wavelet domain Wavelet transforms Wiener filter |
| title | Image Noise Removal via Wavelet Transform and r/K-PSO |
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