The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Meneroka maklumat struktur seperti sebelum imej muka ialah isu utama resolusi super muka (SR). Walaupun rangkaian neural convolutional dalam (CNN) memiliki keupayaan perwakilan yang kuat, cara menggunakan maklumat struktur muka dengan tepat masih menjadi cabaran. Dalam makalah ini, kami mencadangkan rangkaian gabungan sisa baharu untuk menggunakan maklumat struktur berskala untuk muka SR. Berbeza daripada kaedah sedia ada untuk meningkatkan kedalaman rangkaian, modul perhatian kesesakan diperkenalkan untuk mengekstrak ciri struktur muka halus dengan meneroka korelasi daripada peta ciri. Akhir sekali, skala hierarki maklumat struktur digabungkan untuk menghasilkan imej muka resolusi tinggi (HR). Keputusan percubaan menunjukkan rangkaian yang dicadangkan mengatasi beberapa algoritma muka SR muka berasaskan CNN yang terkini.
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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Salinan
Yu WANG, Tao LU, Zhihao WU, Yuntao WU, Yanduo ZHANG, "Face Super-Resolution via Hierarchical Multi-Scale Residual Fusion Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 9, pp. 1365-1369, September 2021, doi: 10.1587/transfun.2020EAL2103.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2103/_p
Salinan
@ARTICLE{e104-a_9_1365,
author={Yu WANG, Tao LU, Zhihao WU, Yuntao WU, Yanduo ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Face Super-Resolution via Hierarchical Multi-Scale Residual Fusion Network},
year={2021},
volume={E104-A},
number={9},
pages={1365-1369},
abstract={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.},
keywords={},
doi={10.1587/transfun.2020EAL2103},
ISSN={1745-1337},
month={September},}
Salinan
TY - JOUR
TI - Face Super-Resolution via Hierarchical Multi-Scale Residual Fusion Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1365
EP - 1369
AU - Yu WANG
AU - Tao LU
AU - Zhihao WU
AU - Yuntao WU
AU - Yanduo ZHANG
PY - 2021
DO - 10.1587/transfun.2020EAL2103
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2021
AB - 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.
ER -