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DC poleHodnotaJazyk
dc.contributor.authorAzizi, Amir-
dc.contributor.authorCharambous, Panayiotis-
dc.contributor.authorChrysanthou, Yiorgos-
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-27T18:31:28Z-
dc.date.available2024-07-27T18:31:28Z-
dc.date.issued2024-
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 107-116.cs_CZ
dc.identifier.issn2464–4625 (online)-
dc.identifier.issn2464–4617 (print)-
dc.identifier.urihttp://hdl.handle.net/11025/57383-
dc.format10 s.cs_CZ
dc.format.mimetypeapplication/pdf
dc.language.isoencs_CZ
dc.publisherVáclav Skala - UNION Agencycs_CZ
dc.subjectzpracování obrazucs_CZ
dc.subjectrekonstrukce obrazucs_CZ
dc.subjectanalýza hlavních komponentcs_CZ
dc.subjectkonvoluční variační automatické kodérycs_CZ
dc.titleImproving Image Reconstruction using Incremental PCA-Embedded Convolutional Variational Auto-Encodercs_CZ
dc.typeconferenceObjectcs_CZ
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedTraditional image reconstruction methods often face challenges like noise, artifacts, and blurriness, requiring handcrafted algorithms for effective resolution. In contrast, deep learning techniques, notably Convolutional Neural Networks (CNNs) and Variational Autoencoders (VAEs), present more robust alternatives. This paper presents a novel and efficient approach for image reconstruction employing Convolutional Variational Autoen coders (CVAEs). We use Incremental Principal Component Analysis (IPCA) to enhance efficiency by discerning and capturing significant features within the latent space. This model is integrated into both the encoder and sampling stages of CVAEs, refining their capability to generate high-fidelity images. Our incremental strategy mitigates scalability issues associated with traditional PCA while preserving the model’s aptitude for identifying crucial image features. Experimental validation utilizing the MNIST dataset showcases noteworthy reductions in processing time and enhancements in image quality, underscoring the efficacy and potential applicability of our model for large-scale image generation tasks.cs_CZ
dc.subject.translatedimage processingcs_CZ
dc.subject.translatedimage reconstructioncs_CZ
dc.subject.translatedprincipal component analysiscs_CZ
dc.subject.translatedConvolutional Variational Auto-encoderscs_CZ
dc.type.statusPeer revieweden
Vyskytuje se v kolekcích:WSCG 2024: Full Papers Proceedings

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