Digital Image Enhancement and Reconstruction

369 Pages·2022·18.721 MB·English

by  Shyam Singh Rajput, Nafis Uddin Khan, Amit Kumar Singh, Karm Veer Arya

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
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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