Facial image super-resolution guided by adaptive geometric features

Facial image super-resolution guided by adaptive geometric features

Abstract This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR...

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Bibliographic Details
Main Authors: Zhenfeng Fan, Xiyuan Hu, Chen Chen, Xiaolian Wang, Silong Peng
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Convolutional neural networks (CNNs)
Depth map
Face super-resolution
Online Access:http://link.springer.com/article/10.1186/s13638-020-01760-y

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