【欠曝光图像增强方法研究的国内外文献综述2200字】

[20]等人提出使用非的欠曝光图像/正常曝光图像来训练网络以消除神经网络对成对图像数据的依赖。这些基于深度学习的方法相比于前述的方法在性能上有很大的提升。1.4研究评述综合来看，现有研究在极弱光成像，恢复清晰细节，对比度增强以及自然的色彩增强效果等方面已经取得了诸多成果，每种方法针对以往的方法都有改进，但大部分欠曝光图像增强过程都及其依赖特定的场景例如照明度等等，离开特定的场景图形增强算法的效果就大打折扣。基于此，我选择了基于深度学习的欠曝光图像增强方法，因为在此基础上构建的网络能充分发挥神经网络学习数据集的优点，最大程度地覆盖不同场景，不同对比度，曝光的图像，以达到设计网络的强适应性和可移植性。参考文献LinL,WangR,WangW,etal.Alow-lightimageenhancementmethodforbothdenoisingandcontrastenlarging[C]//IEEEInternationalConferenceonImageProcessing.IEEE,2015.S.C.F.Lin,C.Y.Wong,M.A.Rahman,G.Jiang,S.Liu,NgaimingKwok,HaiyanShi,Ying-HaoYu,TonghaiWu.Imageenhancementusingtheaveraginghistogramequalization(AVHEQ)approachforcontrastimprovementandbrightnesspreservation[J].ComputersandElectricalEngineering,2015,46.SujeeR,PadmavathiS.Imageenhancementthroughpyramidhistogrammatching[C]//InternationalConferenceonComputerCommunication&Informatics.IEEE,2017:1-5.LandEH.TheRetinexTheoryofColorVision[J].ScientificAmerican,1978,237(6):108-128.JobsonDJ,RahmanZ,WoodellGA.Propertiesandperformanceofacenter/surroundretinex.[J].IEEEtransactionsonimageprocessing:apublicationoftheIEEESignalProcessingSociety,1997,6(3).JobsonDJ,RahmanZ,WoodellGA.Amultiscaleretinexforbridgingthegapbetweencolorimagesandthehumanobservationofscenes.[J].IEEEtransactionsonimageprocessing:apublicationoftheIEEESignalProcessingSociety,1997,6(7).YingZ,GeL,WenG.ABio-InspiredMulti-ExposureFusionFrameworkforLow-lightImageEnhancement[J].2017.GuoXiaojie,LiYu,LingHaibin.LIME:Low-lightImageEnhancementviaIlluminationMapEstimation.[J].IEEEtransactionsonimageprocessing:apublicationoftheIEEESignalProcessingSociety,2016.Zhen-ZhongWU.ResearchonImageEnhancementAlgorithmBasedonRetinexTheory.ModernComputer,2016.BychkovskyV,ParisS,ChanE,etal.Learningphotographicglobaltonaladjustmentwithadatabaseofinput/outputimagepairs[C]//ComputerVisionandPatternRecognition(CVPR),2011IEEEConferenceon.IEEE,2011.YanJ,LinS,KangSB,etal.ALearning-to-RankApproachforImageColorEnhancement[C]//CVPR20