Digital Image Enhancement and Reconstruction PDF
Preview Digital Image Enhancement and Reconstruction
DIGITAL IMAGE ENHANCEMENT AND RECONSTRUCTION Hybrid Computational Intelligence for Pattern Analysis and Understanding Series DIGITAL IMAGE ENHANCEMENT AND RECONSTRUCTION SeriesEditors SIDDHARTHABHATTACHARYYA NILANJANDEY Editedby SHYAMSINGHRAJPUT NAFISUDDINKHAN AMITKUMARSINGH KARMVEERARYA AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom Copyright©2023ElsevierInc.Allrightsreserved. MATLAB®isatrademarkofTheMathWorks,Inc.andisusedwithpermission. TheMathWorksdoesnotwarranttheaccuracyofthetextorexercisesinthisbook. Thisbook’suseordiscussionofMATLAB®softwareorrelatedproductsdoesnotconstitute endorsementorsponsorshipbyTheMathWorksofaparticularpedagogicalapproachorparticular useoftheMATLAB®software. Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans, electronicormechanical,includingphotocopying,recording,oranyinformationstorageand retrievalsystem,withoutpermissioninwritingfromthepublisher.Detailsonhowtoseek permission,furtherinformationaboutthePublisher’spermissionspoliciesandourarrangements withorganizationssuchastheCopyrightClearanceCenterandtheCopyrightLicensingAgency, canbefoundatourwebsite:www.elsevier.com/permissions. Thisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythe Publisher(otherthanasmaybenotedherein). Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperience broadenourunderstanding,changesinresearchmethods,professionalpractices,ormedical treatmentmaybecomenecessary. Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgein evaluatingandusinganyinformation,methods,compounds,orexperimentsdescribedherein.In usingsuchinformationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyof others,includingpartiesforwhomtheyhaveaprofessionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors, assumeanyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproducts liability,negligenceorotherwise,orfromanyuseoroperationofanymethods,products, instructions,orideascontainedinthematerialherein. ISBN:978-0-323-98370-9 ForinformationonallAcademicPresspublications visitourwebsiteathttps://www.elsevier.com/books-and-journals Publisher:MaraE.Conner EditorialProjectManager:IvyDawnTorre ProductionProjectManager:SreejithViswanathan CoverDesigner:ChristianBilbow TypesetbyVTeX Contents Listofcontributors xi Preface xvii Acknowledgments xxi 1. Videoenhancementandsuper-resolution 1 ZhihanLv,JingyiWu,ShuxuanXie,andAnnaJiaGander 1.1. Introduction 1 1.2. Recentrelatedwork 3 1.3. Analysisoflow-qualityvideoIEmodelbasedonDLalgorithm 6 1.4. Resultsanddiscussion 21 1.5. Conclusion 25 Acknowledgment 26 References 26 2. Onestimatinguncertaintyoffingerprintenhancementmodels 29 InduJoshi,AyushUtkarsh,RiyaKothari,VinodK.Kurmi,AntitzaDantcheva, SumantraDuttaRoy,andPremKumarKalra 2.1. Introduction 29 2.2. Relatedwork 32 2.3. Modeluncertaintyestimation 39 2.4. Datauncertaintyestimation 43 2.5. Experimentalevaluation 46 2.6. Resultsandanalysis 49 2.7. Conclusion 65 References 67 3. Hardwareandsoftwarebasedmethodsforunderwaterimage enhancementandrestoration 71 MonikaMathur,NidhiGoel,andGauravBhatnagar 3.1. Introduction 71 3.2. Literaturesurvey 74 3.3. Researchgaps 87 3.4. Conclusionandfuturescope 90 References 90 v vi Contents 4. Denoisingandenhancementofmedicalimagesbystatistically modelingwaveletcoefficients 95 SimaSahu,AmitKumarSingh,AmritKumarAgrawal,andHaoxiangWang 4.1. Introduction 95 4.2. Literaturesurvey 96 4.3. Backgroundandbasicprinciples 98 4.4. Methodology 102 4.5. Simulationresults 103 4.6. Conclusion 110 References 112 5. Medicalimagedenoisingusingconvolutionalneuralnetworks 115 RiniSmitaThakur,ShubhojeetChatterjee,RamNarayanYadav,and LalitaGupta 5.1. Introduction 115 5.2. Differentmedicalimagingmodalities 117 5.3. Convolutionalneuralnetworks 122 5.4. ReviewonexistingCNNdenoisers 125 5.5. Resultanddiscussion 132 5.6. ChallengesofCNN’sdenoisers 133 5.7. Conclusion 134 References 135 6. Multimodallearningofsocialimagerepresentation 139 FeiranHuang,WenxiaoLiu,ZhiyingLi,andWeichangHuang 6.1. Introduction 139 6.2. Representationlearningmethodsofsocialmediaimages 140 6.3. Conclusion 148 References 149 7. Underwaterimageenhancement:past,present,andfuture 151 SurendraNagar,AnkushJain,andPramodKumarSingh Listofabbreviations 151 7.1. Introduction 151 7.2. Underwaterenvironment 153 7.3. Underwaterimageenhancementmethods 154 7.4. Underwaterimagedatasets 160 7.5. Underwaterimagequalityassessment 163 Contents vii 7.6. Challengesandfuturerecommendations 166 7.7. Conclusion 167 References 168 8. Acomparativeanalysisofimagerestorationtechniques 173 SrishtyDwivedi,RamNarayanYadav,andLalitaGupta 8.1. Introduction 173 8.2. Reasonsfordegradationinimage 174 8.3. Imagerestorationtechniques 176 8.4. Performanceanalysisofimagerestoration 200 8.5. Performanceanalysis 201 8.6. Conclusion 206 References 206 9. Comprehensivesurveyoffacesuper-resolutiontechniques 213 AnuragSinghTomar,K.V.Arya,ShyamSinghRajput,andCiroR.Rodriguez 9.1. Introduction 213 9.2. Faceimagedegradationmodel 214 9.3. Classificationoffacehallucinationmethods 215 9.4. Assessmentcriteriaoffacesuper-resolutionalgorithm 227 9.5. Issuesandchallenges 229 9.6. Conclusion 230 References 231 10.Fusion-basedbacklitimageenhancementandanalysisofresults usingcontrastmeasureandSSIM 235 GauravYadav,DilipKumarYadav,andP.V.S.S.R.ChandraMouli 10.1. Introduction 235 10.2. BasicHVScharacteristics 239 10.3. Methodology 242 10.4. Resultdiscussion 244 10.5. Summary 249 References 249 11.Recenttechniquesforhyperspectralimageenhancement 253 AbhishekSingh,K.V.Arya,VineetKansal,andManishGaur 11.1. Introduction 253 11.2. Themajorobjectiveofhyperspectralimageenhancement 255 viii Contents 11.3. Recenttechniquesinhyperspectralimageenhancementby compressingtheimage 257 11.4. Advantagesofhyperspectralimagingovermultispectralimaging 262 11.5. Applicationofhyperspectralimage 262 11.6. Conclusion 264 References 264 12.ClassificationofCOVID-19andnon-COVID-19lungcomputed tomographyimagesusingmachinelearning 269 KhinWeeLai,CaiYeeChang,andWeiKitLoo 12.1. Introduction 269 12.2. Methodology 272 12.3. Results 276 12.4. Discussions 278 12.5. Limitationsandfutureimprovements 288 12.6. Conclusion 289 Acknowledgments 291 References 291 13.BraintumorimagesegmentationusingK-meansandfuzzy C-meansclustering 293 Munish Bhardwaj, Nafis Uddin Khan, Vikas Baghel, SantoshKumarVishwakarma,andAbulBashar 13.1. Introduction 293 13.2. Braintumorextractionusingimagesegmentation 295 13.3. ReviewonK-meansclustering 298 13.4. ReviewonfuzzyC-meansclustering 303 13.5. Performanceanalysisandassessment 307 13.6. Observationsanddiscussions 309 13.7. Conclusions 312 References 312 14.Multimodalitymedicalimagefusioninshearletdomain 317 ManojDiwakar,PrabhishekSingh,andPardeepKumar 14.1. Introduction 317 14.2. Multimodalitymedicalimagefusion 319 14.3. Proposedmethodology 322 14.4. Resultsanddiscussion 322 Contents ix 14.5. Conclusions 325 References 326 15.IIITMFaces:anIndianfaceimagedatabase 329 K.V.AryaandShyamSinghRajput 15.1. Introduction 329 15.2. Relatedwork 331 15.3. Methodology 332 15.4. Experimentalsetup 336 15.5. Results 338 15.6. Conclusions 339 Acknowledgments 339 References 339 Index 343
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