基于粗糙集和反锐化掩模的图像增强.研究

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1、thresholds，givingfourdifferentregionsofbrightregion，darkregion，noiseregionandnoiselessregion．Togetherwithapproximateandequivalentrelationsinroughset，thenwecanremovethenoiseeffectivelywiththedebuggedthresholds．Latermergingthebrightanddarkregionwhichhavebeennoiseremovedwecangetanoiselessimage．Finally

2、wedecomposethenoiselessimageintodifferentlayersbywaveletanalysis．Thereisonelowfrequentapproximateimageaswellasthreehighfrequentdetailedimagesineverylayer．Thenweenhancethehighfrequentdetailedimagesbyunsharpmaskingaccordingtohumans’visionpeculiarity．Bringtheenhancementtooutlineanddetailedinformationi

3、ntoeffect．Sofarwefinishthewholeenhancingprocess．Inordertoprovetheeffectivenessandadvantageofthisnewmethoddesignedinthispaper，Wemadesubsectionsimulationandcomprehensivesimulationinthispaper．First，wemadeenhancingsimulationcomparebyroughsetandothercommonnoiseremovingmethodstoanimageonlywithnoiseintera

4、ct．Secondwemadeenhancingsimulationcomparebywaveletunsharpmaskingandotherunsharpmethodstoanimageonlyhasfuzzyproblem；ThirdwemadeacomprehensivesimulationcomparebythenewmethoddesignedinthispaperandothertraditionalmethodstoahumanbrainCTimageandbeerbottlesimagewithcracks，bothofwhichwithnoiseandfuzzyprobl

5、ems．Experimentresultsshowthatatfirstnoiseremovingeffectbyroughsetdesignedinthispaperismuchbetterthantraditionalnoiseremovemethodsnotonlyinsubjectivevisionresultbutalsoinobjectivenoisetosignalratio．Seconddetailedinformationenhancedeffectbywaveletunsharpmaskingdesignedinthispaperisalsobetterthanother

6、unsharpresults．Lastthecomprehensivesimulationresulttoimagebothwithnoiseandfuzzyproblemsshowsthatusingroughsetandwaveletunsharpmaskingdesignedherestillcanreachabetterresultthancommonenhancingwaysnotonlyinsubjectivevisionresultbutalsoinobjectivenoisetosignalratio，notonlyitcanremovenoiseeffectively,th

7、edetailedinformationandedgescouldbeimprovedalso，obtainingasatisfactionenhancingresult．KEYWORDS：imageenhancement，roughset，waveletanalysis，unsharpmaskingV基于粗糙集和反锐化掩模的图像增强研究原创性声明及关于学位论文使用授权的声明原创性声明本人郑重声明：所呈交的学