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dc.contributor.authorHuang, HCen_US
dc.contributor.authorChen, CMen_US
dc.contributor.authorWang, SDen_US
dc.contributor.authorLu, HHSen_US
dc.date.accessioned2014-12-08T15:43:21Z-
dc.date.available2014-12-08T15:43:21Z-
dc.date.issued2001-10-10en_US
dc.identifier.issn0003-6935en_US
dc.identifier.urihttp://hdl.handle.net/11536/29344-
dc.description.abstractTwo new noise-reduction algorithms, namely, the adaptive symmetric mean filter (ASMF) and the hybrid filter, are presented in this paper. The idea of the ASMF is to find the largest symmetric region on a slope facet by incorporation of the gradient similarity criterion and the symmetry constraint into region growing. The gradient similarity criterion allows more pixels to be included for a statistically better estimation, whereas the symmetry constraint promises an unbiased estimate if the noise is completely removed. The hybrid filter combines the advantages of the ASMF, the double-window modified-trimmed mean filter, and the adaptive mean filter to optimize noise reduction on the step and the ramp edges. The experimental results have shown the ASMF and the hybrid filter are superior to three conventional filters for the synthetic and the natural images in terms of the root-mean-squared error, the root-mean-squared difference of gradient, and the visual presentation. (C) 2001 Optical Society of America.en_US
dc.language.isoen_USen_US
dc.titleAdaptive symmetric mean filter: A new noise-reduction approach based on the slope facet modelen_US
dc.typeArticleen_US
dc.identifier.journalAPPLIED OPTICSen_US
dc.citation.volume40en_US
dc.citation.issue29en_US
dc.citation.spage5192en_US
dc.citation.epage5205en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000171406500007-
dc.citation.woscount2-
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