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2017 | OriginalPaper | Buchkapitel

Face Video Super-Resolution with Identity Guided Generative Adversarial Networks

verfasst von : Dingyi Li, Zengfu Wang

Erschienen in: Computer Vision

Verlag: Springer Singapore

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Abstract

Faces are of particular concerns in video surveillance systems. It is challenging to reconstruct clear faces from low-resolution (LR) videos. In this paper, we propose a new method for face video super-resolution (SR) based on identity guided generative adversarial networks (GANs). We establish a two-stage convolutional neural network (CNN) for face video SR, and employ identity guided GANs to recover high-resolution (HR) facial details. Extensive experiments validate the effectiveness of our proposed method from the following aspects: fidelity, visual quality and robustness to pose, expression and illuminance variations.

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Vorheriges Kapitel A New Image Sparse Reconstruction Method for Mixed Gaussian-Poisson Noise with Multiple Constraints

Nächstes Kapitel Exemplar-Based Pixel by Pixel Inpainting Based on Patch Shift

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Titel: Face Video Super-Resolution with Identity Guided Generative Adversarial Networks
verfasst von: Dingyi Li
Zengfu Wang
Verlag: Springer Singapore
Buch: Computer Vision
Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-7302-1_30

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