圖像增強(qiáng)技術(shù)外文翻譯參考文獻(xiàn)綜述

圖像增強(qiáng)技術(shù)外文翻譯參考文獻(xiàn)綜述圖像增強(qiáng)技術(shù)外文翻譯參考文獻(xiàn)綜述(文檔含中英文對照即英文原文和中文翻譯)原文：HybridGeneticAlgorithmBasedImageEnhancementTechnologyAbstract—inimageenhancement,TubbsproposedanormalizedincompleteBetafunctiontorepresentseveralkindsofcommonlyusednon-lineartransformfunctionstodotheresearchonimageenhancement.ButhowtodefinethecoefficientsoftheBetafunctionisstillaproblem.WeproposedaHybridGeneticAlgorithmwhichcombinestheDifferentialEvolutiontotheGeneticAlgorithmintheimageenhancementprocessandutilizethequicklysearchingabilityofthealgorithmtocarryouttheadaptivemutationandsearches.FinallyweusetheSimulationexperimenttoprovetheeffectivenessofthemethod.Keywords-Imageenhancement;HybridGeneticAlgorithm;adaptiveenhancementI.INTRODUCTIONIntheimageformation,transferorconversionprocess,duetootherobjectivefactorssuchassystemnoise,inadequateorexcessiveexposure,relativemotionandsotheimpactwillgettheimageoftenadifferencebetweentheoriginalimage(referredtoasdegradedordegraded)Degradedimageisusuallyblurredoraftertheextractionofinformationthroughthemachinetoreduceorevenwrong,itmusttakesomemeasuresforitsimprovement.Imageenhancementtechnologyisproposedinthissense,andthepurposeistoimprovetheimagequality.FuzzyImageEnhancementsituationaccordingtotheimageusingavarietyofspecialtechnicalhighlightssomeoftheinformationintheimage,reduceoreliminatetheirrelevantinformation,toemphasizetheimageofthewholeorthepurposeoflocalfeatures.Imageenhancementmethodisstillnounifiedtheory,imageenhancementtechniquescanbedividedintothreecategories:pointoperations,andspatialfrequencyenhancementmethodsEnhancementAct.Thispaperpresentsanautomaticadjustmentaccordingtotheimagecharacteristicsofadaptiveimageenhancementmethodthatcalledhybridgeneticalgorithm.Itcombinesthedifferentialevolutionalgorithmofadaptivesearchcapabilities,automaticallydeterminesthetransformationfunctionoftheparametervaluesinordertoachieveadaptiveimageenhancement.II.IMAGEENHANCEMENTTECHNOLOGYImageenhancementreferstosomefeaturesoftheimage,suchascontour,contrast,emphasisorhighlightedges,etc.,inordertofacilitatedetectionorfurtheranalysisandprocessing.Enhancementswillnotincreasetheinformationintheimagedata,butwillchoosetheappropriatefeaturesoftheexpansionofdynamicrange,makingthesefeaturesmoreeasilydetectedoridentified,forthedetectionandtreatmentfollow-upanalysisandlayagoodfoundation.Imageenhancementmethodconsistsofpointoperations,spatialfiltering,andfrequencydomainfilteringcategories.Pointoperations,includingcontraststretching,histogrammodeling,andlimitingnoiseandimagesubtractiontechniques.Spatialfilterincludinglow-passfiltering,medianfiltering,highpassfilter(imagesharpening).Frequencyfilterincludinghomomorphismfiltering,multi-scalemulti-resolutionimageenhancementapplied[1].III.DIFFERENTIALEVOLUTIONALGORITHMDifferentialEvolution(DE)wasfirstproposedbyPriceandStorn,andwithotherevolutionaryalgorithmsarecompared,DEalgorithmhasastrongspatialsearchcapability,andeasytoimplement,easytounderstand.DEalgorithmisanovelsearchalgorithm,itisfirstinthesearchspacerandomlygeneratestheinitialpopulationandthencalculatethedifferencebetweenanytwomembersofthevector,andthedifferenceisaddedtothethirdmemberofthevector,bywhichMethodtoformanewindividual.Ifyoufindthatthefitnessofnewindividualmembersbetterthantheoriginal,thenreplacetheoriginalwiththeformationofindividualself.TheoperationofDEisthesameasgeneticalgorithm,anditconcludemutation,crossoverandselection,butthemethodsaredifferent.WesupposethatthegroupsizeisP,thevectordimensionisD,andwecanexpresstheobjectvectoras(1):xi=[xi1，xi2，…，xiD](i=1,…,P)(1)Andthemutationvectorcanbeexpressedas(2):i=1,...,P(2),,arethreerandomlyselectedindividualsfromgroup,andr1r2r3i.Fisarangeof[0,2]betweentheactualtypeconstantfactordifferencevectorisusedtocontroltheinfluence,commonlyreferredtoasscalingfactor.Clearlythedifferencebetweenthevectorandthesmallerthedisturbancealsosmaller,whichmeansthatifgroupsclosetotheoptimumvalue,thedisturbancewillbeautomaticallyreduced.DEalgorithmselectionoperationisa"greedy"selectionmode,ifandonlyifthenewvectoruithefitnessoftheindividualthanthetargetvectorisbetterwhentheindividualxi,uiwillberetainedtothenextgroup.Otherwise,thetargetvectorxiindividualsremainintheoriginalgroup,onceagainasthenextgenerationoftheparentvector.IV.HYBRIDGAFORIMAGEENHANCEMENTIMAGEenhancementisthefoundationtogetthefastobjectdetection,soitisnecessarytofindreal-timeandgoodperformancealgorithm.Forthepracticalrequirementsofdifferentsystems,manyalgorithmsneedtodeterminetheparametersandartificialthresholds.Canuseanon-completeBetafunction,itcancompletelycoverthetypicalimageenhancementtransformtype,buttodeterminetheBetafunctionparametersarestillmanyproblemstobesolved.ThissectionpresentsaBetafunction,sinceaccordingtotheapplicablemethodforimageenhancement,adaptiveHybridgeneticalgorithmsearchcapabilities,automaticallydeterminesthetransformationfunctionoftheparametervaluesinordertoachieveadaptiveimageenhancement.Thepurposeofimageenhancementistoimproveimagequality,whicharemoreprominentfeaturesofthespecifiedrestorethedegradedimagedetailsandsoon.Inthedegradedimageinacommonfeatureisthecontrastlowersideusuallypresentsbright,dimorgrayconcentrated.Low-contrastdegradedimagecanbestretchedtoachieveadynamichistogramenhancement,suchasgraylevelchange.WeuseIxytoillustratethegraylevelofpoint(x,y)whichcanbeexpressedby(3).Ixy=f(x,y)(3)where:“f”isalinearornonlinearfunction.Ingeneral,grayimagehavefournonlineartranslations[6][7]thatcanbeshownasFigure1.WeuseanormalizedincompleteBetafunctiontoautomaticallyfitthe4categoriesofimageenhancementtransformationcurve.Itdefinesin(4):(4)where:(5)Fordifferentvalueofαandβ,wecangetresponsecurvefrom(4)and(5).ThehybridGAcanmakeuseoftheprevioussectionadaptivedifferentialevolutionalgorithmtosearchforthebestfunctiontodetermineavalueofBeta,andtheneachpixelgrayscalevaluesintotheBetafunction,thecorrespondingtransformationofFigure1,resultinginidealimageenhancement.Thedetaildescriptionisfollows:Assumingtheoriginalimagepixel(x,y)ofthepixelgraylevelbytheformula(4),denotedby,,hereΩistheimagedomain.EnhancedimageisdenotedbyIxy.Firstly,theimagegrayvaluenormalizedinto[0,1]by(6).(6)where:andexpressthemaximumandminimumofimagegrayrelatively.Definethenonlineartransformationfunctionf(u)(0≤u≤1)totransformsourceimagetoGxy=f(),wherethe0≤Gxy≤1.Finally,weusethehybridgeneticalgorithmtodeterminetheappropriateBetafunctionf(u)theoptimalparametersαandβ.WillenhancetheimageGxytransformedantinormalized.V.EXPERIMENTANDANALYSISInthesimulation,weusedtwodifferenttypesofgray-scaleimagesdegraded;theprogramperformed50times,populationsizesof30,evolved600times.Theresultsshowthattheproposedmethodcanveryeffectivelyenhancethedifferenttypesofdegradedimage.Figure2,thesizeoftheoriginalimagea320×320,it'sthecontrasttolow,andsomedetailsofthemoreobscure,inparticular,scarvesandotherdetailsofthetextureisnotobvious,visualeffects,poor,usingthemethodproposedinthissection,toovercometheabovesomeoftheissuesandgetsatisfactoryimageresults,asshowninFigure5(b)shows,thevisualeffectshavebeenwellimproved.Fromthehistogramview,thescopeofthedistributionofimageintensityismoreuniform,andthedistributionoflightanddarkgrayareaismorereasonable.Hybridgeneticalgorithmtoautomaticallyidentifythenonlineartransformationofthefunctioncurve,andthevaluesobtainedbefore9.837,5.7912,fromthecurvecanbedrawn,itisconsistentwithFigure3,c-class,thatstretchacrossthemiddleregioncompressiontransformtheregion,whichwereconsistentwiththehistogram,theoveralloriginalimagelowcontrast,compressionatbothendsofthemiddleregionstretchingregionisconsistentwithhumanvisualsense,enhancedtheeffectofsignificantlyimproved.Figure3,thesizeoftheoriginalimagea320×256,theoverallintensityislow,theuseofthemethodproposedinthissectionaretheimagesb,wecanseetheground,chairsandclothesandotherdetailsoftheresolutionandcontrastthantheoriginalimagehasImprovedsignificantly,theoriginalimagegraydistributionconcentratedinthelowerregion,andtheenhancedimageofthegrayuniform,graybeforeandaftertransformationandnonlineartransformationofbasicgraph3(a)thesameclass,namely,theimageDimregionstretching,andthevalueswere5.9409,9.5704,nonlineartransformationofimagesdegradedtypeinferenceiscorrect,theenhancedvisualeffectandgoodrobustnessenhancement.Difficulttoassessthequalityofimageenhancement,imageisstillnocommonevaluationcriteria,commonpeaksignaltonoiseratio(PSNR)evaluationintermsofline,butthepeaksignaltonoiseratiodoesnotreflectthehumanvisualsystemerror.Therefore,weusemarginalprotectionindexandcontrastincreaseindextoevaluatetheexperimentalresults.EdgelProtectionIndex(EPI)isdefinedasfollows:(7)

ContrastIncreaseIndex(CII)isdefinedasfollows:(8)Infigure4,wecomparedwiththeWaveletTransformbasedalgorithmandgettheevaluatenumberinTABLEI.Figure4(a,c)showtheoriginalimageandthedifferentialevolutionalgorithmforenhancedresultscanbeseenfromtheenhancedcontrastmarkedlyimproved,clearerimagedetails,edgefeaturemoreprominent.b,cshowsthewavelet-basedhybridgeneticalgorithm-basedComparisonofImageEnhancement:wavelet-basedenhancementmethodtoenhanceimagedetailoutsomeoftheimagevisualeffectisanimprovementovertheoriginalimage,buttheenhancementisnotobvious;andHybridgeneticalgorithmbasedonadaptivetransformimageenhancementeffectisverygood,imagedetails,texture,clarityisenhancedcomparedwiththeresultsbasedonwavelettransformhasgreatlyimprovedtheimageofthepost-analyticalprocessinghelpful.Experimentalenhancementexperimentusingwavelettransform"sym4"wavelet,enhanceddifferentialevolutionalgorithmexperiment,theparametersandthevalueswere5.9409,9.5704.Fora256×256sizeimagetransformbasedonadaptivehybridgeneticalgorithminMatlab7.0imageenhancementsoftware,thecomputingtimeisabout2seconds,operationisveryfast.FromTABLEI,objectiveevaluationcriteriacanbeseen,boththeedgeoftheprotectionindex,ortoenhancethecontrastindex,basedonadaptivehybridgeneticalgorithmcomparedtotraditionalmethodsbasedonwavelettransformhasalargerincrease,whichisfromThissectiondescribestheobjectiveadvantagesofthemethod.Fromaboveanalysis,wecanseethatthismethod.Fromaboveanalysis,wecanseethatthismethodcanbeusefulandeffective.VI.CONCLUSIONInthispaper,tomaintaintheintegrityoftheperspectiveimageinformation,theuseofHybridgeneticalgorithmforimageenhancement,canbeseenfromtheexperimentalresults,basedontheHybridgeneticalgorithmforimageenhancementmethodhasobviouseffect.Comparedwithotherevolutionaryalgorithms,hybridgeneticalgorithmoutstandingperformanceofthealgorithm,itissimple,robustandrapidconvergenceisalmostoptimalsolutioncanbefoundineachrun,whilethehybridgeneticalgorithmisonlyafewparametersneedtobesetandthesamesetofparameterscanbeusedinmanydifferentproblems.UsingtheHybridgeneticalgorithmquicksearchcapabilityforagiventestimageadaptivemutation,search,tofinalizethetransformationfunctionfromthebestparametervalues.Andtheexhaustivemethodcomparedtoasignificantreductioninthetimetoaskandsolvethecomputingcomplexity.Therefore,theproposedimageenhancementmethodhassomepracticalvalue.REFERENCES[1]HEBinetal.,VisualC++DigitalImageProcessing[M],Posts&TelecomPress,2001,4:473～477[2]StornR,PriceK.DifferentialEvolution—aSimpleandEfficientAdaptiveSchemeforGlobalOptimizationoverContinuousSpace[R].InternationalComputerScienceInstitute,Berlaey,1995.[3]TubbsJD.Anoteonparametricimageenhancement[J].PatternRecognition.1997,30(6):617-621.[4]TANGMing,MASongDe,XIAOJing.EnhancingFarInfraredImageSequenceswithModelBasedAdaptiveFiltering[J].CHINESEJOURNALOFCOMPUTERS,2000,23(8):893-896.[5]ZHOUJiLiu,LVHang,ImageEnhancementBasedonANewGeneticAlgorithm[J].ChineseJournalofComputers,2001,24(9):959-964.[6]LIYun,LIUXuecheng.OnAlgorithmofImageConstractEnhancementBasedonWaveletTransformation[J].ComputerApplicationsandSoftware,2008,8.[7]XIEMei-hua,WANGZheng-ming,ThePartialDifferentialEquationMethodforImageResolutionEnhancement[J].JournalofRemoteSensing,2005，9(6):673-679.基于混合遺傳算法的圖像增強(qiáng)技術(shù)摘要—在圖像增強(qiáng)之中，塔布斯提出了歸一化不完全β函數(shù)表示常用的幾種使用的非線性變換函數(shù)對圖像進(jìn)行研究增強(qiáng)。但如何確定Beta系數(shù)功能仍然是一個問題。在圖像增強(qiáng)處理和利用遺傳算法快速算法的搜索能力進(jìn)行自適應(yīng)變異和搜索我們提出了一種混合遺傳將微分進(jìn)化算法。最后利用仿真實(shí)驗(yàn)證明了該方法的有效性。關(guān)鍵詞—圖像增強(qiáng)；混合遺傳算法；自適應(yīng)增強(qiáng)Ⅰ.介紹在圖像形成，傳遞或轉(zhuǎn)換過程，由于其他客觀因素，如系統(tǒng)噪聲，不足或過度曝光，相對運(yùn)動等的影響會使圖像通常與原始圖像之間有差別（簡稱退化或退化）。退化圖像通常模糊或信息的提取通過機(jī)器后減少甚至是錯誤的，它必須采取一些改進(jìn)措施。圖像增強(qiáng)技術(shù)是在其目的是為了提高圖像的質(zhì)量這個意義上提出的。模糊圖像增強(qiáng)情況是根據(jù)圖像使用各種特殊技術(shù)集錦的一些信息圖像，減少或消除不相關(guān)的信息，來強(qiáng)調(diào)整體或局部特征的目標(biāo)圖像。圖像增強(qiáng)方法仍沒有統(tǒng)一的理論，圖像增強(qiáng)技術(shù)可分為三類別：點(diǎn)運(yùn)算，與空間頻率增強(qiáng)方法增強(qiáng)法。本文介紹了根據(jù)圖像特征自動調(diào)整自適應(yīng)圖像增強(qiáng)方法，稱為混合遺傳算法。為了實(shí)現(xiàn)圖像的自適應(yīng)增強(qiáng)它結(jié)合了差分進(jìn)化自適應(yīng)搜索算法，自動確定的參數(shù)值的變換函數(shù)。Ⅱ.圖像增強(qiáng)技術(shù)圖像增強(qiáng)是圖像的某些特征，如輪廓，對比，強(qiáng)調(diào)或突出的邊緣等為了便于檢測和進(jìn)一步的分析和處理.增強(qiáng)將不會增加圖像中的信息數(shù)據(jù)，但會選擇適當(dāng)?shù)膭討B(tài)范圍的功能的擴(kuò)展，使得這些特點(diǎn)更容易檢測或確定，為后續(xù)的分析和處理的檢測打下良好的基礎(chǔ)。圖像增強(qiáng)方法包括點(diǎn)運(yùn)算，空間濾波，頻域?yàn)V波類別。點(diǎn)運(yùn)算包括對比度拉伸，直方圖建模，并限制噪聲和圖像減影技術(shù)?？臻g濾波器包括低通濾波，中值濾波，高通濾波器（銳化）。頻率濾波器包括同態(tài)濾波，多尺度多分辨率圖像增強(qiáng)中的應(yīng)用[1]。Ⅲ.差分進(jìn)化算法差分進(jìn)化（DE）首次提出了強(qiáng)硬的價值，并與其他進(jìn)化算法進(jìn)行比較，DE算法具有強(qiáng)大的空間搜索能力，易實(shí)現(xiàn)，容易理解。DE算法是一種新型的搜索算法，它首先是在搜索空間中隨機(jī)產(chǎn)生初始種群，然后計算之間的任何差異向量的兩個成員，所不同的添加到向量的第三個成員，通過該方法，形成一個新的個人。如果你發(fā)現(xiàn)新的個體成員比原來的好，然后替換原來的個體，自我的形成。DE操作作為遺傳算法一樣，它結(jié)論突變，交叉和選擇，但方法是不同的。我們假設(shè)組的大小是P，矢量維D，我們可以表達(dá)的目標(biāo)向量為（1）：xi=[xi1，xi2，…，xiD](i=1,…,P)(1)變異向量可以表示為（2）：i=1,...,P(2),,是三個從群中隨機(jī)選擇的個人，其中，r1r2r3i。F是一系列的[0，2]之間的實(shí)際類型的用于控制影響的常數(shù)因子差異向量，通常被稱為比例因子。顯然，矢量之間的區(qū)別越小則干擾也越小，這意味著如果組接近最佳值，擾動會自動降低。DE算法的選擇操作是一個“貪婪”的選擇模式，當(dāng)且僅當(dāng)新的矢量Ui比目標(biāo)向量Xi更好更健全，Ui將被保留到下一組。否則，目標(biāo)向量Xi留在原來的組，再次作為下一代的父矢量。Ⅳ.圖像增強(qiáng)圖像的混合遺傳算法增強(qiáng)是獲得快速對象檢測的基礎(chǔ)，因此有必要尋找實(shí)時性能好的算法。對不同系統(tǒng)的實(shí)際要求，許多算法需要確定的參數(shù)和人工閾值。它可以使用一個非完全Beta函數(shù)來完全覆蓋典型變換式的圖像增強(qiáng)，但確定Beta函數(shù)參數(shù)仍有許多亟待解決的問題。本節(jié)介紹了一種Beta功能，因?yàn)楦鶕?jù)適用的圖像增強(qiáng)的方法，自適應(yīng)混合遺傳算法的搜索的能力，自動確定變換命令的參數(shù)值來實(shí)現(xiàn)圖像增強(qiáng)的自適應(yīng)功能。

圖像增強(qiáng)的目的是提高圖像質(zhì)量，是在指定的比較突出的特點(diǎn)恢復(fù)退化圖像細(xì)節(jié)等。一個共同的特征的退化圖像通常是對比的下側(cè)呈明亮的，暗淡或灰色濃。低對比度退化圖像可拉伸達(dá)到一種動態(tài)的直方圖增強(qiáng)，如灰度變化。我們用Ixy來說明點(diǎn)（x，y）的灰度級它可以是由（3）表示。Ixy=f(x,y)(3)其中：“f”為一個線性或非線性函數(shù)。在一般情況下，灰圖像有四個非線性的翻譯[6][7]，可以是如圖1所示。我們采用歸一化的Beta函數(shù)自動適應(yīng)4類圖像增強(qiáng)轉(zhuǎn)變曲線。（4）中定義：(4)其中：(5)對于不同的α，β值，我們可以從（4）及（5）中得到響應(yīng)曲線。圖1四種傳統(tǒng)的翻譯該混合算法可以利用前面的部分自適應(yīng)差分進(jìn)化算法搜索最佳函數(shù)來確定的β值，然后每個像素灰度值為β函數(shù)，相應(yīng)的圖1轉(zhuǎn)化，產(chǎn)生理想的圖像增強(qiáng)。詳細(xì)描述如下：假設(shè)原始圖像的像素（x，y）的像素的灰度水平，表示為式（4），記為，，這里是圖像域。增強(qiáng)的圖像由Ixy表示。首先，圖像的灰度值在（6）中歸到[0，1]。(6)其中：imax和imin表示圖像灰度的最大值和最小值。定義非線性變換函數(shù)f（U）（0≤U≤1）變換成源圖像GXY=f（GXY），其中，0≤GXY≤1。最后，我們使用了混合遺傳算法來確定適當(dāng)?shù)腂eta函數(shù)f（U）的最佳參數(shù)α和β。V.實(shí)驗(yàn)和分析在模擬中，我們使用兩種不同類型的灰度圖像退化；程序執(zhí)行了50次，人口大小為30，進(jìn)化600次。結(jié)果表明，提出的方法可以非常有效地提高不同退化圖像類型。a)原始圖像b)增強(qiáng)圖像圖2單個圖像增強(qiáng)過程a)原始圖像b)增強(qiáng)圖像圖3移動對象增強(qiáng)過程圖2，原始圖像為320×320的大小，它是對比度低，和更為模糊的一些細(xì)節(jié)，特別的，外圍和其他細(xì)節(jié)很不明顯，視覺效果差，使用文中提出的方法部分，克服了以上的一些問題，并得到令人滿意的圖像效果，如圖5（b）顯示，該視覺效果得到明顯改善。從直方圖看來，圖像的強(qiáng)度分布的范圍是比較均勻，光明與黑暗的灰色區(qū)域的分布更合理了。混合遺傳算法自動確定函數(shù)曲線的非線性變換，從曲線可以得出值9.837,5.7912，它符合圖3的C級，跨越壓縮變換的中間區(qū)域，這與直方圖相一致，整體的原始圖像低對比度，在中間區(qū)域兩端壓縮拉伸區(qū)域與人的視覺一致，增強(qiáng)效果明顯提高。圖3，原始圖像的大小320×25，整體強(qiáng)度低，使用文中提出的方法得到b圖像，我們可以看到地上，椅子和衣服和其他細(xì)節(jié)的分辨率和對比度比原始圖像有明顯改善，原始圖像的灰度分布集中在較低的區(qū)域，其增強(qiáng)的灰度圖像的灰度均勻，圖3（a）之前和之后基本的變換和非線性變換是一樣的，即，圖像暗區(qū)伸展的值是5.9409,9.5704，非線性變換的圖像退化類型推斷是正確的，增強(qiáng)視覺效果和良好的圖像增強(qiáng)效應(yīng)。圖像還沒有一個統(tǒng)一的評價標(biāo)準(zhǔn)則很難評價圖像質(zhì)量的提高，有共同峰值信號噪聲比（PSNR）方面的評價，但峰值信噪比不反映人類視覺系統(tǒng)誤差。因此，我們利用邊緣保護(hù)指數(shù)與對比增長指數(shù)評價實(shí)驗(yàn)結(jié)果。edgel保護(hù)指數(shù)（EPI）的定義如下（7）：(7)

對比度增加指數(shù)（CII）定義如下：(