完整後設資料紀錄
DC 欄位語言
dc.contributor.authorLu, Shih-Maoen_US
dc.contributor.authorLiang, Sheng-Fuen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2014-12-08T15:16:18Z-
dc.date.available2014-12-08T15:16:18Z-
dc.date.issued2006-07-01en_US
dc.identifier.issn1016-2364en_US
dc.identifier.urihttp://hdl.handle.net/11536/12083-
dc.description.abstractIn this paper, a novel two-stage noise removal algorithm to deal with salt-pepper impulse noise is proposed. In the first stage, the decision-based recursive adaptive noise-exclusive median filter is applied to remove the noise cleanly and to keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify image pixels into human perception sensitive class and non-sensitive class. A neural network is proposed to compensate the sensitive regions for image quality enhancement. According to the experimental results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions of the resultant images.en_US
dc.language.isoen_USen_US
dc.subjectsalt-pepperen_US
dc.subjectimpulse noiseen_US
dc.subjectnoise removalen_US
dc.subjectfuzzy decision systemen_US
dc.subjecthuman visual systemen_US
dc.subjectneural networken_US
dc.titleA HVS-directed neural-network-based approach for salt-pepper impulse noise removalen_US
dc.typeArticleen_US
dc.identifier.journalJOURNAL OF INFORMATION SCIENCE AND ENGINEERINGen_US
dc.citation.volume22en_US
dc.citation.issue4en_US
dc.citation.spage925en_US
dc.citation.epage939en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000239532700013-
dc.citation.woscount1-
顯示於類別:期刊論文


文件中的檔案:

  1. 000239532700013.pdf

若為 zip 檔案,請下載檔案解壓縮後,用瀏覽器開啟資料夾中的 index.html 瀏覽全文。