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Erschienen in:
A Brief Review of Image Denoising Algorithms and Beyond Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising

verfasst von : Le Manh Quan, Yong-Guk Kim

Erschienen in: Inpainting and Denoising Challenges

Verlag: Springer International Publishing

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Abstract

The sparse autoencoder stacked inside deep neural network has been a powerful tool for image inpainting. We propose a new method for video inpainting as well as fingerprint denoising based on the Iterative Application of Autoencoders (IAA). Instead of using either a single sparse autoencoder or denoising autoencoder, multiple autoencoders are concatenated in an iterative manner until a desired output is acquired from the last stage. This method allows us to reduce loss via iteration and reuse a well-defined network. Results from two public challenges on video inpainting and fingerprint denoising suggest that performance is excellent and it can be a useful approach for image inpainting in general. Our codes are available online.

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Vorheriges Kapitel FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks
Nächstes Kapitel Video DeCaptioning Using U-Net with Stacked Dilated Convolutional Layers
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Metadaten
Titel
Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising
verfasst von
Le Manh Quan
Yong-Guk Kim
Copyright-Jahr
2019
Buch
Inpainting and Denoising Challenges

Print ISBN: 978-3-030-25613-5

Electronic ISBN: 978-3-030-25614-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-25614-2_5

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