Název:	Improving Image Reconstruction using Incremental PCA-Embedded Convolutional Variational Auto-Encoder
Autoři:	Azizi, Amir Charambous, Panayiotis Chrysanthou, Yiorgos
Citace zdrojového dokumentu:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 107-116.
Datum vydání:	2024
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	conferenceObject
URI:	http://hdl.handle.net/11025/57383
ISSN:	2464–4625 (online) 2464–4617 (print)
Klíčová slova:	zpracování obrazu;rekonstrukce obrazu;analýza hlavních komponent;konvoluční variační automatické kodéry
Klíčová slova v dalším jazyce:	image processing;image reconstruction;principal component analysis;Convolutional Variational Auto-encoders
Abstrakt v dalším jazyce:	Traditional 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.
Vyskytuje se v kolekcích:	WSCG 2024: Full Papers Proceedings

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.