Improving Image Reconstruction using Incremental PCA-Embedded Convolutional Variational Auto-Encoder

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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.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.en
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 107-116.en
dc.identifier.doihttps://doi.org/10.24132/CSRN.3401.12
dc.identifier.issn2464–4625 (online)
dc.identifier.issn2464–4617 (print)
dc.identifier.urihttp://hdl.handle.net/11025/57383
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.rights.accessopenAccessen
dc.subjectzpracování obrazucs
dc.subjectrekonstrukce obrazucs
dc.subjectanalýza hlavních komponentcs
dc.subjectkonvoluční variační automatické kodérycs
dc.subject.translatedimage processingen
dc.subject.translatedimage reconstructionen
dc.subject.translatedprincipal component analysisen
dc.subject.translatedConvolutional Variational Auto-encodersen
dc.titleImproving Image Reconstruction using Incremental PCA-Embedded Convolutional Variational Auto-Encoderen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer revieweden
dc.type.versionpublishedVersionen

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