Color Image and Video Enhancement PDF
Preview Color Image and Video Enhancement
Color Image and Video Enhancement M. Emre Celebi • Michela Lecca • Bogdan Smolka Editors Color Image and Video Enhancement 1 C Editors M.EmreCelebi BogdanSmolka LouisianaStateUniversity SilesianUniversityofTechnology Shreveport Gliwice Louisiana Poland USA MichelaLecca FondazioneBrunoKessler CenterforInformationandCommunicationTechnology Trento Italy ISBN978-3-319-09362-8 ISBN978-3-319-09363-5(eBook) DOI10.1007/978-3-319-09363-5 LibraryofCongressControlNumber:2015943686 SpringerChamHeidelbergNewYorkDordrechtLondon (cid:2)c SpringerInternationalPublishingSwitzerland2015 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped. 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Printedonacid-freepaper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) Preface Enhancement of digital images and video sequences is the process of increasing the quality of the visual information by improving its visibility and perceptibil- ity.Enhancement isanecessarystepinimage/video processingapplications when the conditions under which a scene is captured result in quality degradation, e.g., increased/decreased brightness and/or contrast, distortion of colors, and introduc- tionofnoiseandotherartifactssuchasblotchesandstreaks.Unfortunately,mostof thetraditionalenhancementmethodsaredesignedformonochromaticimage/video data.Themultivariatenatureofcolorimage/videodatapresentsconsiderablechal- lenges for researchers and practitioners as the numerous methods developed for singlechanneldataareoftennotdirectlyapplicabletomultichanneldata. Thegoalofthisvolumeistosummarizethestate-of-the-artincolorimageand video enhancement. The intended audience includes researchers and practitioners, whoareincreasinglyusingcolorimagesandvideos. The volume opens with two chapters related to image acquisition. In “Colori- metricCharacterisation,”Westlandfocusesontheproblemofcolorreproductionin devicessuchascameras,monitors,andprinters.Theauthordescribescolorspaces mainly used for representing colors by consumer technologies currently available, analyzesthedeviceaccuracyonthereproductionofreal-worldcolors,andillustrates variouscolorcorrectionmethodsformatchingthecolorgamutsofdifferentdevices. In“ImageDemosaicing,”ZhenandStevensonpresentanoverviewofdemosaicking methods. The authors introduce the fundamentals of interpolation and analyze the structure of various state-of-the-art approaches. In addition, they elaborate on the advantages and disadvantages of the examined techniques and evaluate their per- formance using popular image quality metrics. Finally, they discuss demosaicing combinedwithdeblurringandsuper-resolution. Thevolumecontinueswithtwochaptersonnoiseremoval.In“DCT-BasedColor Image Denoising: Efficiency Analysis and Prediction,” Lukin et al. discuss image denoising techniques based on the discrete cosine transform (DCT). The authors analyze noise models, discuss various image quality measures, describe various typesoffilters,andintroducetheconceptofimageenhancementutilizingtheDCT. v vi Preface In “Impulsive Noise Filters for Colour Images,” Morillas et al. give an overview of the impulsive noise reduction methods for color images. They analyze various models of impulsive noise contamination, introduce quality metrics used for the evaluationoffilteringeffectiveness,discussvariousmethodsofvectorordering,and analyzethemaintypesofnoisereductionalgorithms.Theauthorsnotonlydescribe variousapproachestoimpulsivenoisereduction,butalsoevaluatetheireffectiveness andsummarizetheirmainproperties. The volume continues with seven chapters on color/contrast enhancement. In “SpatialandFrequency-BasedVariationalMethodsforPerceptuallyInspiredColor and Contrast Enhancement of Digital Images,” Provenzi considers perceptually inspiredcolorcorrectionalgorithmsthataimtoreproducethecolorsensationpro- ducedbythehumanvisionsystem.Thesealgorithmsarebasedonthewell-known Retinex model, introduced by Land and McCann about 45 years ago. The author showsthatRetinex-likeapproachescanbeembeddedinageneralvariationalframe- work, where these methods can be interpreted as a local, nonlinear modification of histogram equalization. In “The Color Logarithmic Image Processing (CoLIP) Antagonist Space,” Gavet et al. present a survey of Color Logarithmic Image Processing, a perceptually-oriented mathematical framework for representing and processingcolorimages.Theauthorsalsopresentvariousapplicationsofthisframe- workrangingfromcontrastenhancement tosegmentation.In“ColorManagement andVirtualRestorationofArtworks,”MainoandMontipresentasurveyoftheuse ofcolorandcontrastenhancementtechniquesinthevirtualrestorationofartworks suchaspaintings,mosaics,ancientarchivaldocuments,andmanuscripts.Histogram equalization approaches, Retinex-like methods, and multi-spectral image process- ing algorithms are essential tools to analyse an artwork, to discover its history, to measure its conservation/degradation status, and to plan future physical restora- tion. The authors provide examples of applications of such digital techniques on several well-known Italian artworks. In “A GPU-Accelerated Adaptive Simulta- neous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement,” Tsai and Huang propose an adaptive dynamic range compression algorithm for color image enhancement. The authors demonstrate that a CUDA implementation of the proposed algorithm achieves up to 700% speed up when executed on an NVIDIA NVS 5200M GPU compared to a LUT-accelerated implementation executed on an Intel Core i7-3520M CPU. In “ColorEqualizationandRetinex,”Wangetal.giveanoverviewofseveralpercep- tuallyinspiredcolorcorrectionalgorithmsthatattempttosimulatethehumancolor constancy capability. The authors first describe two histogram equalization meth- odsthatmodifytheimagecolorsbymanipulatingrespectivelytheglobalandlocal color distributions. They then illustrate an automatic color equalization approach thatenhancesthecolorandcontrastofanimagebycombiningtheGray-Worldand White-Patch models. Finally, they describe the Retinex model and various imple- mentations of it. In “Color Correction for Stereo and Multi-View Coding,” Fezza andLarabifirstpresentasurveyofcolorcorrectionmethodsformulti-viewvideo. Theythencomparethequantitative/qualitativeperformanceofsomeofthepopular Preface vii methodswithrespecttocolorconsistency,codingperformance,andrenderingqual- ity. Finally, in “Enhancement of Image Content for Observers with Colour Vision Deficiencies,” Milic´ et al. present a survey of daltonization methods designed for enhancing the perceptual quality of color images for the benefit of observers with colorvisiondeficiencies. In “Computationally Efficient Data and Application Driven Color Transforms fortheCompressionandEnhancementofImagesandVideo,”Minervinietal.deal with the problem of efficient coding and transmissionof color images and videos. The RGB data recorded by camera sensors are typically redundant due to high correlation of the color channels. The authors describe two frameworks to obtain linear maps of the RGB data that minimize the loss of information due to com- pression. The first adapts to the image data and aims at reconstruction accuracy, representing an efficient approximation of the classic Karhunen-Loève transform. The second adapts to the application in which the images are used, for instance, animageclassificationtask.Achapterentitled“OverviewofGrayscaleImageCol- orizationTechniques,”byPopowiczandSmolkacompletesthevolume.Theauthors firstpresentasurveyofsemi-automaticgrayscaleimagecolorizationmethods.They then compare the performance of three semi-automatic and one fully-automatic methodonavarietyofimages.Finally,theyproposeamethodologyforevaluating colorizationmethodsbasedonseveralwell-knownqualityassessmentmeasures. Aseditors,wehopethatthisvolumefocusedoncolorimageandvideoenhance- mentwilldemonstratethesignificantprogressthathasoccurredinthisfieldinrecent years.Wealsohopethatthedevelopmentsreportedinthisvolumewillmotivatefur- therresearchinthisexcitingfield. M.EmreCelebi MichelaLecca BogdanSmolka Contents 1 ColorimetricCharacterization................................... 1 StephenWestland 2 ImageDemosaicing............................................. 13 RuiwenZhenandRobertL.Stevenson 3 DCT-Based Color Image Denoising: Efficiency Analysis and Prediction..................................................... 55 VladimirLukin,SergeyAbramov,RuslanKozhemiakin,AlexeyRubel, Mikhail Uss, Nikolay Ponomarenko, Victoriya Abramova, Benoit Vozel,KacemChehdi,KarenEgiazarianandJaakkoAstola 4 ImpulsiveNoiseFiltersforColourImages ........................ 81 Samuel Morillas, Valentín Gregori, Almanzor Sapena, Joan-GerardCamarenaandBernardinoRoig 5 SpatialandFrequency-BasedVariationalMethodsforPerceptually InspiredColorandContrastEnhancementofDigitalImages........131 EdoardoProvenzi 6 TheColorLogarithmicImageProcessing(CoLIP)AntagonistSpace.155 YannGavet,JohanDebayleandJean-CharlesPinoli 7 Color Management and Virtual Restoration ofArtworks ...................................................183 GiuseppeMainoandMariapaolaMonti 8 A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-TimeColorImageEnhancement............................233 Chi-YiTsaiandChih-HungHuang ix x Contents 9 ColorEqualizationandRetinex..................................253 LiqianWang,LiangXiaoandZhihuiWei 10 ColorCorrectionforStereoandMulti-viewCoding................291 SidAhmedFezzaandMohamed-ChakerLarabi 11 EnhancementofImageContentforObserverswithColourVision Deficiencies....................................................315 NedaMilic´,DragoljubNovakovic´ andBrankoMilosavljevic´ 12 OverviewofGrayscaleImageColorizationTechniques .............345 AdamPopowiczandBogdanSmolka 13 Computationally Efficient Data and Application Driven Color TransformsfortheCompressionandEnhancementofImagesand Video .........................................................371 MassimoMinervini,CristianRusuandSotiriosA.Tsaftaris Contributors SergeyAbramov NationalAerospaceUniversity,Kharkov,Ukraine VictoriyaAbramova NationalAerospaceUniversity,Kharkov,Ukraine JaakkoAstola TampereUniversityofTechnology,Tampere,Finland Joan-GerardCamarena Conselleriad’Educacio,Valencia,Spain KacemChehdi UniversityofRennes1,Lannion,France JohanDebayle ÉcoleNationaleSupérieuredesMines,Saint-Etienne,France KarenEgiazarian TampereUniversityofTechnology,Tampere,Finland SidAhmedFezza UniversityofOran2,Oran,Algeria YannGavet ÉcoleNationaleSupérieuredesMines,Saint-Etienne,France ValentínGregori InstitutoUniversitariodeMatemáticaPurayAplicada,Univer- sitatPolitècnicadeValència,Valencia,Spain Chih-Hung Huang Department of Electrical Engineering, Tamkang University, TamsuiDistrict,NewTaipeiCity,TaiwanR.O.C. RuslanKozhemiakin NationalAerospaceUniversity,Kharkov,Ukraine Mohamed-Chaker Larabi XLIM Institute, SIC Department, University of Poitiers,Poitiers,France VladimirLukin NationalAerospaceUniversity,Kharkov,Ukraine xi
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