图像去噪英文文献及翻译

1、NewMethodforImageDenoisingwhileKeepingEdgeInformationEdgeinformationisthemostimportanthigh-frequencyinformationofanimage,soweshouldtrytomaintainmoreedgeinformationwhiledenoising.Inordertopreserveimagedetailsaswellascancelingimagenoise,wepresentanewimagedenoisingmethod:imagedenoisingbasedonedgedetect

2、ion.Beforedenoising,imagesedgesarefirstdetected,andthenthenoisedimageisdividedintotwoparts:edgepartandsmoothpart.Wecanthereforesethighdenoisingthresholdtosmoothpartoftheimageandlowdenoisingthresholdtoedgepart.Thetheoreticalanalysesandexperimentalresultspresentedinthispapershowthat,comparedtocommonly

3、-usedwaveletthresholddenoisingmethods,theproposedalgorithmcouldnotonlykeepedgeinformationofanimage,butalsocouldimprovesignal-to-noiseratioofthedenoisedimage.Inthewaveletdomain,thedenoisingalgorithmbasedonthethresholdfilteriswidelyused,becauseitscomparativelyefficientandeasytorealize.Wecanselectathre

4、sholdaccordingtothecharacteristicoftheimage,modifyingallofthediscretedetailcoefficientssoastoreducethenoise.However,weareinthedilemmaofdeterminingthelevelofthethreshold.Thehigherthethresholdis,thebettereffectofdenoisingwillbe,and,atthesametime,theblurriertheedgewillbe.Theedgesofanimagemostlyreflectt

5、heinformationoftheimage,andcontainitsbasiccharacter.Accordingtoresearchonhumaneyes,thecharacteristicoftheedgesisoneofseveralcharacteristicsthatcanstronglyimpressthevisualsystem.Thus,whenweprocessdenoising,thefirstthingthatweshouldcareaboutistryingtoretainedgeinformation.Thispaperpresentsanewmethodfo

6、rimagedenoisingwhilekeepingedgeinformation.Wefirstapplywavelettransformtoanoisedimage,andthenprocessedgedetection.Thewaveletcoefficientsaredividedintotwoparts:edgepartandsmoothpart.Wecanthereforesethighdenoisingthresholdtothesmoothpartandlowdenoisingthresholdtoedgepartinordertoretainmoreedgeinformat

7、ion.Thetheoreticanalysisandexperimentalresultspresentedinthispapershowsthat,comparedwithcommonly-usedwaveletthresholddenoisingmethods,theproposeddenoisingmethodismoreeffective.Theideaofcombiningedgedetectionwithdenoisingisdoable.Therestofthispaperisorganizedasfollows.Wepresenttheproposeddenoisingmet

8、hodinSection2.ExperimentalresultstodemonstratetheperformanceoftheproposedmethodaregiveninSection3,andconclusionsandcommentsaregiveninSection4.ThispaperdiscusseshowtoremovetheadditivewhiteGaussiannoise(AWGN)withazeromean.Forotherkindsofnoisemodeling,theideaofthispaperisalsoapplicable.Thedenoisingmeth

9、odwepresentneedstodetecttheimagesedgesbeforedenoising,soastoprotecttheimagesedgeinformationfromdamageinthefollowingdenoisingprocess.Inourmethod,findingoutthepreciselocationoftheedgesispivotal.Manyclassicaledgedetectorsarealreadyavailable.Edgescanbedeterminedfromtheimagebyprocessingdirectlyinthespati

10、aldomainorbytransformationtoadifferentdomain.Inthespatialdomain,thereareSobeledgeoperators,Prewittedgeoperators,Kirschedgeoperators,andsoon.Inthetransformingfield,wavelettransformationisadaptedtothewildly-changededgesbetterthanwiththenormalFouriertransformation.Wavelettransformation,whichiscalledthe

11、“mathematicalmicroscope,”hasaresolutioninboththetimefieldandthefrequencyfield.Itcanfocusontoanydetailoftheanalyzedobjectbytakingmoreandmorefinestepsofthespacefield.owingtothesecharacteristics,wavelettransformisverysuitableforuseinedgedetection.Inthispartwepresentanimageedgedetectionmethodbasedonwave

12、lettransformation.WhenimagesarecorruptedbyAWGN,duetonoise,somepixelsofthehomogeneousregionsmayalsohavealocalmaximumofthegradientmodulus,soweshoulddistinguishthecoefficientscorrespondingtonoisefromthosecorrespondingtothepotentialedges.WeknowthattheLipschitzexponentvaluesofAWGNarealwaysnegative,sothev

13、alueofitscorrespondinglocalmaximumofthegradientmoduluswilldiminishathigherscales.Thisisdifferentfromtheedgesoftheimage,whichalwayshavepositiveLipschitzexponentvalues.Asaresult,wecanwipeoffsomecoefficientscorrespondingtonoisebyusingthesedifferentattributions.Furthermore,wecanconnecttheremainingcoeffi

14、cientsalongtheedgeorientation,whichisverticaltothegradientdirection.Thosethatcannotbeconnectedwillbeconsideredascoefficientscorrespondingtonoise,andthenwillbewipedoff.Inpractice,weshouldpayattentiontothefollowing：ThelengthofthefilterusedinDWTshouldnotbetoolong;otherwise,itwillaffecttheeffectofedgede

15、tection.Theboundaryshouldbetreatedproperly.Inourexperiment,weuseamirror-symmetricalextension.Theedgedetectingprocedureiscomposedofthefollowingstages:applypretreatmenttotheimage,usingtheaveragefilteranddenotingtheresultingimagef(x,y)。applytheredundantwavelettransformationtoeachrowFindthelocalmaximumc

16、oefficientsofeveryrow.Recordthesecoefficientsf(x,y).RemovethecoefficientswithlowLipschitzexponentvaluesfromtherecordedcoefficients,becausetheycorrespondtonoise.Thus,wecangetthecoefficientscorrespondingtothepotentialedgesofeachrowatdifferentscales.Applyingstage1,2,3,and4toeverycolumn,wecangetthecoeff

17、icientscorrespondingtothepotentialedgesofeachcolumnatdifferentscales.Notethatthewaveletcoefficientsinfactcorrespondtothegradientofthesmoothedversionoffatthescale.Theedgemagnitudesandorientationcanbecalculatedfromtheimagegradientasfollows:Jointherecordedcoefficientsofsimilaredgemagnitudesalongtheedge

18、orientationinachain.Thoseisolatedcoefficientsarewipedoff.WhenthelengthofthechainreachesthethresholdT,thepixelscorrespondingtothewaveletcoefficientsinthechainareconsideredtobeedgepixels.Weappliedouredgedetectingtechniquetoa256*256LenaimagecorruptedbyAWGN.ALenaimageisanimagewithrelativelycomplexedges.

19、Itisdifficultfornormaledgedetectiontocompletelydetectthedifferenttypesofedges.Withanoise-corruptedLenaimage,theedgedetectiontaskisevenmoredifficult.Themethodwepresentusestheadvantagesofwavelettransformation,whichcanfocusontoanydetailoftheanalyzedobjectbytakingmoreandmorefinestepsofthespacefield.Atth

20、elowscale,manydetailsoftheedges,suchasthegirlspupils,aredetected;atahighscale,smoothlongeredges,suchasthepoleontheleft,areseen.Theexperimentalresultsshownprovethatouredgedetectingmethodiseffective.Afterwavelettransformation,mostenergyofsignalissupposedtobeclusteredinafewwaveletcoefficients,whereasno

21、isesarenot.Thethresholding,orshrinkageonthewaveletcoefficientswithaproperthreshold,canthensignificantlyreducenoise.Thekeypointofwaveletthresholddenoisingisselectingaproperthresholdthehigherthethresholdis,thebettereffectofdenoisingwillbe,and,atthesametime,theblurriertheedgewillbe.Ourdenoisingmethodis

22、focusedonsolvingthisproblem.Beforedenoising,thosewaveletcoefficientsofanimagethatcorrespondtoanimagesedgesarefirstdetectedbythemethodofwaveletedgedetection.Thedetectedwaveletcoefficientswillbeprotectedfromtheensuingdenoisingprocess,and,therefore,wecansetthedenoisingthresholdsbasedsolelyonthenoisevar

23、iances,withoutworryingaboutdamagingtheimagesedges.Inourexperiment,wechoosetheVisuShinkthreshold，Theprocedureiscomposedofthefollowingsixstages:Detectthewaveletcoefficientscorrespondingtotheimagesedgesbythemethodofwaveletedgedetection.Preservethecoefficientscorrespondingtotheedges.Applywavelettransfor

24、mtotheoriginalnoise-corruptedimage.Dothenormalwaveletimagethresholdnoisingprocess.Intheequation,TpresentsVisuShinkthresholdHere，Replacethecoefficientscorrespondingtotheedgeswiththepreservedcoefficients.Thedetectededgesalsocontainnoise,sotheymustbedenoisedtoo.Hereweagainusewaveletdenoisingbasedonthet

25、hresholdfilter,butamuchlowerthreshold,T,isappliedinordertomaintainmoreedgeinformation.Byapplyingthereversewavelettransformation,wecangetthedenoisedimage.WeappliedthreedenoisingmethodstoimagesthathadbeencorruptedbywhiteGaussiannoisewithazeromeananddifferentvariances(seeFig.2).Thethreemethodsare:theme

26、thodwepresent,theclassicalimagewaveletthresholddenoising,andtheclassicalimagewaveletthresholddenoising,TableIIshowstheexperimentalresults.givestheresultingdenoisingimages.Fromthetableandthefigures,wecanseethat,withtheclassicaldenoisingmethod,itisdifficulttodecidethevalueofthethreshold.WhenweusetheVi

27、suShinkthreshold,thedenoisedimageissmoother,but,atthesametime,moreedgeinformationislost,sotheedgesarenotablyblurred.Whenwelowerthethresholdandmultiplyitbyafactormoreedgeinformationismaintained,butthePSNRvalueisalsolowered.Thus,withtheclassicaldenoisingmethod,itisadilemmatodeterminethelevelofthethres

28、hold.Thehigherthethresholdis,thebettereffectofdenoisingwillbe,and,atthesametime,theblurriertheedgewillbe.Inthedenoisingmethodwhichwepresent,thosewaveletcoefficientsofanimagethatcorrespondtoanimagesedgesarefirstdetectedbythemethodofwaveletedgedetectionbeforedenoising.Thedetectedwaveletcoefficientswil

29、lthenbeprotectedfromdenoising,andwecanthereforesetthedenoisingthresholdsbasedonlyonthenoisevariancesandwithoutdamagingtheimagesedges.Thetheoreticalanalysisandexperimentalresultspresentedinthispapershowthat,comparedwiththecommonly-usedwaveletthresholddenoisingmethods,ourmethodcankeepanimagesedgesfrom

30、damageandincreasethePSNRupto12dB.Imagedenoisingviawavelettransformisonesuccessofwaveletapplications.Becauseofitssimplealgorithmandsmallcomputationquantity,denoisingbythresholdingcanobtainthewidespreadapplication.Bothedgeandnoiseinformationarehigh-frequencyinformation,sothelossofedgeinformationisevid

31、entandinevitableinthedenoisingprocess.Ifwecombineedgedetectionwithdenoising,wecanovercometheshortcomingofcommonly-useddenoisingmethodsanddodenoisingwithoutnotablyblurringtheedge.Furthermore,therearemanydenoisingandedgedetectionmethodsnowinuse.Differentmethodsaresuitablefordifferenttypeofimagesandfor

32、differentnoisemodels.Wecandofurtherresearchonhowtocombinethesedifferentdenoisingandedgedetectionmethods,accordingtothecontentoftheimagesandthenatureofthenoise.同时保持边缘信息的图像去噪新方法由于是数字图像，那么对于一幅黑白图像来说，只要把各个像素赋值为0或1即可，我们用1表示白色，用0表示黑色，于是我们把一幅黑白图像称为二值图像，彩色图像或其它图像转化为黑白图像的过程叫做二值化。对于一幅彩色图像，每个像素我们都需要用3个取值范围为错误!

33、未找到引用源。之间的整数值来分别表示红、绿、蓝三原色分量，且这些分量都是用整型数据表示，称之为像素颜色的R,GB值。表示一个取值范围为错误!未找到引用源。的整型数据，需要占用8bit空间，三个R,G,B这样的整型数据就需要用24bit来存储，所以，我们常把一幅真彩色位图称为24位位图。边缘信息的图像是最重要的高频信息，所以我们应该在去噪的时候尽量保持更多的边缘信息。为了保持图像细节以及消除图像噪声，我们提出了一种新的图像去噪方法：基于边缘检测的图像去噪。在去噪之前，首先先检测图像的边缘，降噪后的图像被划分成两个部分：边缘部分和平滑部分。因此，我们可以设置给平滑部分比较高的去噪阈值，边缘部分低的

34、去噪阈值。本文提出的理论分析和实验结果，常用的小波阈值去噪方法相比，该算法不仅能保持图像边缘信息，而且还可以提高去噪图像信号噪声比。在小波域去噪算法的门槛上过滤器被广泛使用，因为它是比较高效，易于实现的。我们可以选择所述阈值的图像的特征，修改所有的离散细节系数，以减少噪声。不过，很难确定准确的阈值。因为在同一时间，阈值越高，去噪效果越好，边缘越模糊。图像的边缘主要反映了图像的信息,包含它的基本特征。根据对人类眼睛的研究，边缘的几个特点之一是可以强烈打动视觉系统。因此,我们在去噪过程首先应该关心的是试图保留边缘信息。因此，去噪处理时，我们应该关心的第一件事就是试图保留边缘信息。本文提出了一种新的

35、方法，能同时保持边缘信息的图像去噪。我们首先运用小波变换处理被噪声污染的图像，然后进行边缘检测。小波系数被划分为两部分：边缘部分和平滑部分。因此，我们可以给平滑部分设置高去噪阈值，给边缘部分设置低去噪阈值，以保留更多的边缘信息。本文提出的理论分析和实验结果表明，与常用的小波阈值去噪方法相比，此去噪方法更有效，同时也证明了边缘检测与去噪相结合的想法是可行的。本文的其余部分安排如下：第2节中，我们提出去噪方法。在第3节用实验结果证明所提出的方法的性能，第4节中给出结论和意见。本文讨论如何去掉一个零均值的加性高斯白噪声(AWGN)。对于其他类型的噪声模型，本文的想法也同样适用。我们提出的去噪方法是去

36、噪前需要检测图像的边缘，从而保护图像的边缘信息不会在去噪过程中损坏。在我们的方法中，找出边缘的精确位置是很重要的。许多经典的边缘探测器已经上市，可以从图像中确定，通过直接在空间域处理，或通过转化到一个不同的域。在空间域中，Sobel算子的边缘算子，Prewitt算子的边缘算子，Kirsch边缘运营商等等。在转化的字段中，小波变换比正常的傅里叶变换能更好地适应多变的边缘。小波变换，就是所谓的“数学显微镜”，在时域和频域都有分辨率。它可以聚焦到任何一个细节的分析对象，通过采取的步骤空间领域越来越细。由于这些特性，小波变换是非常适合在边缘检测中使用。在此，我们提出了基于小波变换的图像边缘检测方法。当

37、图像被加性高斯白噪声损坏时，由于噪声均匀区域的一些像素可能也有梯度模数的局部最大值，所以我们应该区分潜在在边缘的噪声相对应的系数。我们知道加性高斯白噪声利普希茨(Lipschitz)指数值总是负的，所以其相应的本地最大的梯度模量的价值将更大幅度的减少。这不同于图像的边缘总是具有正Lipschitz指数值。因此，我们可以通过使用这些不同的属性擦去一些系数对应的噪声。此外，我们可以连接其余的垂直于梯度方向的沿边缘方向的系数。那些不能被连接的系数将被视为噪声，然后将被擦去。在实践中，我们应注意以下几点：小波变换使用的滤波器的长度不能太长，否则会影响边缘检测的效果。边界应妥善处理。在实验中，我们使用了镜面对称扩展。找到每一行的最大系数，记录这些系数。在记录中删除低李普希茨指数值的系数,因为它符合噪声。因此,我们可以得到在不同的情况下系数对应的每一行的潜在边缘。应用阶段1、2、3和4,每一列,我们可以得到对应于潜在的边缘在不同情况下的每一列的系数。注意，小波系数实际上适用于梯度平滑版本的f(x,y)在级数。大小、边缘定位可以从图像梯度计算如下:7沿边缘链中的方向加入记录的类似的边缘幅度系数。这些离散的系数被擦去。链的长度达到阈值T时，对应于链中的小波系数的像素被认为是边缘像素。应用我们