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Erschienen in:
On the Security Relevance of Initial Weights in Deep Neural Networks Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Fractal Residual Network for Face Image Super-Resolution

verfasst von : Yuchun Fang, Qicai Ran, Yifan Li

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2020

Verlag: Springer International Publishing

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Abstract

Recently, many Convolutional Neural Network (CNN) algorithms have been proposed for image super-resolution, but most of them aim at architecture or natural scene images. In this paper, we propose a new fractal residual network model for face image super-resolution, which is very useful in the domain of surveillance and security. The architecture of the proposed model is composed of multi-branches. Each branch is incrementally cascaded with multiple self-similar residual blocks, which makes the branch appears as a fractal structure. Such a structure makes it possible to learn both global residual and local residual sufficiently. We propose a multi-scale progressive training strategy to enlarge the image size and make the training feasible. We propose to combine the loss of face attributes and face structure to refine the super-resolution results. Meanwhile, adversarial training is introduced to generate details. The results of our proposed model outperform other benchmark methods in qualitative and quantitative analysis.

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Vorheriges Kapitel On the Security Relevance of Initial Weights in Deep Neural Networks
Nächstes Kapitel From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders
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Metadaten
Titel
Fractal Residual Network for Face Image Super-Resolution
verfasst von
Yuchun Fang
Qicai Ran
Yifan Li
Copyright-Jahr
2020
Buch
Artificial Neural Networks and Machine Learning – ICANN 2020

Print ISBN: 978-3-030-61608-3

Electronic ISBN: 978-3-030-61609-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-61609-0_2