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

Yu WANG, Tao LU, Zhihao WU, Yuntao WU, Yanduo ZHANG

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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.

Publication
IEICE TRANSACTIONS on Fundamentals 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

Authors

Yu WANG
  Wuhan Institute of Technology
Tao LU
  Wuhan Institute of Technology
Zhihao WU
  Wuhan Institute of Technology
Yuntao WU
  Wuhan Institute of Technology
Yanduo ZHANG
  Wuhan Institute of Technology

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