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Development of New Image Fusion Techniques / M Munawwar Iqbal Ch

By: Iqbal Ch, M Munawwar Contributor(s): Supervised by Dr. Naima Iltaf Material type: Text

TextPublisher: Rawalpindi, MCS (NUST), 2020Description: xv, 108 pSubject(s): PhD Computer Software Engineering Thesis | PhD CSE ThesisDDC classification: 005.1,IQB

Contents:

The imagefusionprocessaimstocombinethecrucialinformationfrommultipleim- ages obtainedfromdifferentresources.Numerousimagefusiontechniquesarepro- posed toachievebetterfusionaccuracyhowevertheygenerallyyieldlessinformative edge details,introduceartifactsandhaveblurreddetailsinmultifocusimages.More- over,inmedicalimages,resultantfusedimageshavemissingfinedetailsofdifferent tissues, improperfusionofdifferenttissuesandboundariesarealsonotclearlydemar- cated. This workpresents,differentmulti-focusandmulti-modeltechniquestominimize the above-mentionedissues/limitation.Crossbilateralfilterwhichprovidesbetterper- ceptual qualityiscombinedwithnon-subsampledcontourlettransformtomaximize the usefulinformationpresentinmultiplescalesanddirections.Guidedfilterwhose computing timeisindependentoffiltersizeandwellpreservesedgesiscombinedwith discrete wavelettransformformoreinformativeedgedetails.Alphamapalongwith top hattransformextractscommonpropertiesofdifferentimages.Saliencymapshows more meaningfulrepresentationofimagethereforeitisusedalongwithcrossbilat- eral filtertofusemultimodalbrainimagemodalitieswhichhelptocombinethefacts found inmultiplemedicalimagesthathaveexceptionaldistinctivemodalities.Energy of Laplaciananddiscretewavelettransformbasedfusionofmultimodalbrainimages helps toproperlydemarcateboundariesofdifferenttissues.Shiftinvariantdiscrete wavelettransformandsparsefusionisusedformoreinformativeanatomydetailsof medical imageswhichhelpsradiologistsinevaluatingdifferentmodalityimages. Proposed multifocusimagefusionschemesmaintaindifferentimagedetailslikere- duce edgeblurring,introducelessartifactsandpreservesharpedgedetails.Moreover, suggested multimodalimagefusionschemesformedicalimagespreservethefinede- tails ofdifferenttissues,properfusionofdifferenttissuesandclearlydemarcatethe boundaries toobtainhighqualityfusedimage.Higherquantitativeandqualitativeout- comes areobservedintheproposedframeworksforfusionschemesascomparedto other existingschemes. In generalourresearchworkpresentsmulti-focusandmulti-modelimagefusion problems intwomaindomainsi.e.photographyapplications(toovercometheproblem of depthoffieldofcameras)andbiomedicalimaging.Theproposednovelandeffec- tivealgorithmshavebeentestedonrealworlddatatodemonstratetheirperformance improvement.

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Item type	Current location	Home library	Shelving location	Call number	Status	Notes	Date due	Barcode	Item holds
Thesis	Military College of Signals (MCS)	Military College of Signals (MCS)	Thesis	005.1,IQB (Browse shelf)	Available	Almirah No.68, Shelf No.5		MCSPhD CSE-12

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The imagefusionprocessaimstocombinethecrucialinformationfrommultipleim-
ages obtainedfromdifferentresources.Numerousimagefusiontechniquesarepro-
posed toachievebetterfusionaccuracyhowevertheygenerallyyieldlessinformative
edge details,introduceartifactsandhaveblurreddetailsinmultifocusimages.More-
over,inmedicalimages,resultantfusedimageshavemissingfinedetailsofdifferent
tissues, improperfusionofdifferenttissuesandboundariesarealsonotclearlydemar-
cated.
This workpresents,differentmulti-focusandmulti-modeltechniquestominimize
the above-mentionedissues/limitation.Crossbilateralfilterwhichprovidesbetterper-
ceptual qualityiscombinedwithnon-subsampledcontourlettransformtomaximize
the usefulinformationpresentinmultiplescalesanddirections.Guidedfilterwhose
computing timeisindependentoffiltersizeandwellpreservesedgesiscombinedwith
discrete wavelettransformformoreinformativeedgedetails.Alphamapalongwith
top hattransformextractscommonpropertiesofdifferentimages.Saliencymapshows
more meaningfulrepresentationofimagethereforeitisusedalongwithcrossbilat-
eral filtertofusemultimodalbrainimagemodalitieswhichhelptocombinethefacts
found inmultiplemedicalimagesthathaveexceptionaldistinctivemodalities.Energy
of Laplaciananddiscretewavelettransformbasedfusionofmultimodalbrainimages
helps toproperlydemarcateboundariesofdifferenttissues.Shiftinvariantdiscrete
wavelettransformandsparsefusionisusedformoreinformativeanatomydetailsof
medical imageswhichhelpsradiologistsinevaluatingdifferentmodalityimages.
Proposed multifocusimagefusionschemesmaintaindifferentimagedetailslikere-
duce edgeblurring,introducelessartifactsandpreservesharpedgedetails.Moreover,
suggested multimodalimagefusionschemesformedicalimagespreservethefinede-
tails ofdifferenttissues,properfusionofdifferenttissuesandclearlydemarcatethe
boundaries toobtainhighqualityfusedimage.Higherquantitativeandqualitativeout-
comes areobservedintheproposedframeworksforfusionschemesascomparedto
other existingschemes.
In generalourresearchworkpresentsmulti-focusandmulti-modelimagefusion
problems intwomaindomainsi.e.photographyapplications(toovercometheproblem
of depthoffieldofcameras)andbiomedicalimaging.Theproposednovelandeffec-
tivealgorithmshavebeentestedonrealworlddatatodemonstratetheirperformance
improvement.

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