JOURNAL ARTICLE
Image Inpainting with Variational Autoencoders
Deesamutara, SanhanatMaggiore, Federico
Year: 2020 Journal: Zenodo (CERN European Organization for Nuclear Research) Publisher: European Organization for Nuclear Research
Abstract
This work presents the training methodology of variational autoencoder in order to restore the missing part of a corrupted image and question the requirement of homogeneity in the implementation and difference situation of corrupted data.
Keywords:
Inpainting Autoencoder Image (mathematics) Pattern recognition (psychology) Image restoration Image processing
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Topics
Generative Adversarial Networks and Image Synthesis
Physical Sciences → Computer Science → Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences → Computer Science → Computer Vision and Pattern Recognition
Face recognition and analysis
Physical Sciences → Computer Science → Computer Vision and Pattern Recognition
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