Please use this persistent identifier to cite or link to this item: doi:10.24405/14267
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dc.contributor.advisorZölzer, Udode_DE
dc.contributor.authorBhattacharya, Purbaditya-
dc.date.accessioned2022-05-09T08:57:15Z-
dc.date.available2022-05-09T08:57:15Z-
dc.date.issued2022-05-
dc.identifier.urihttps://doi.org/10.24405/14267-
dc.description.abstractDeep learning belongs to the family of artificial intelligence and machine learning where the primary objective is to learn and diversify the feature representation for a given system. In deep learning, a machine is able to develop large parameterized models that addresses a plethora of scientific problems based on a number of optimization methods. These models will be capable of retrieving, representing, generating, and combining a large number of features to provide a generalized solution to the intended problems. Unlike traditional machine learning algorithms, deep learning algorithms offer an opportunity to learn, extract, and even generate very large feature spaces via densely parameterized models, which are capable of learning semantic information and an efficient input-output mapping. Hence, they are very suitable in low- level computer vision applications involving multimedia enhancement problems. Deep learning has a very broad scope, but this thesis is primarily focused on artificial neural networks, convolutional neural networks, and their variants which are some of the most powerful deep learning tools today. In this work, the neural network fundamentals are explained, the corresponding derivations are performed, and the workflows are illustrated. Important modules of convolutional neural networks are described and their functions are discussed. Various convolutional architectures are proposed for various computer vision tasks related to image quality improvement and their suitability towards the particular problems are explained. Various networks, which include novel network modules and architectures, are studied and applied in the areas of image and video enhancement. Ablation studies and experiments are performed on the network architectures to analyze them. Finally, the proposed models are evaluated in terms of their prowess towards the aforementioned vision tasks.de_DE
dc.description.sponsorshipAllgemeine Nachrichtentechnikde_DE
dc.language.isoende_DE
dc.publisherUniversitätsbibliothek der HSU/UniBwHde_DE
dc.subjectConvolutional Neural Networkde_DE
dc.subjectFaltendes neuronales Netzwerkde_DE
dc.subjectDeep Learningde_DE
dc.subjectTiefes Lernende_DE
dc.subjectImage Processingde_DE
dc.subjectBildverarbeitungde_DE
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaften::620 Ingenieurwissenschaften und zugeordnete Tätigkeitende_DE
dc.titleDeep Learning for Image Enhancementde_DE
dc.typeThesisde_DE
dcterms.dateAccepted2022-04-25-
dc.contributor.refereeKlauer, Berndde_DE
dcterms.bibliographicCitation.originalpublisherplaceHamburgde_DE
dc.contributor.grantorHSU Hamburgde_DE
dc.type.thesisDoctoral Thesisde_DE
local.submission.typefull-textde_DE
item.grantfulltextopen-
item.fulltext_sWith Fulltext-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairetypeThesis-
crisitem.author.deptAllgemeine Nachrichtentechnik-
crisitem.author.parentorgFakultät für Elektrotechnik-
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