Face Super-Resolution via Hierarchical Multi-Scale Residual Fusion Network

Yu WANG  Tao LU  Zhihao WU  Yuntao WU  Yanduo ZHANG  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E104-A   No.9   pp.1365-1369
Publication Date: 2021/09/01
Publicized: 2021/03/03
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2020EAL2103
Type of Manuscript: LETTER
Category: Image
Keyword: 
multi-scale residual,  bottleneck attention module,  face super-resolution,  

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Summary: 
Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.