Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (1): 1-8, 18.doi: 10.6040/j.issn.1672-3961.0.2021.312

• Machine Learning & Data Mining •     Next Articles

Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction

Tongyu JIANG1(),Fan CHEN2,Hongjie HE1,*()   

  1. 1. Sichuan Key Laboratory of Signal and Information Processing, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
    2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, Sichuan, China
  • Received:2021-06-28 Online:2022-02-20 Published:2022-02-21
  • Contact: Hongjie HE E-mail:jty971018@msn.cn;hjhe@swjtu.edu.cn

Abstract:

A lightweight face super-resolution network was proposed in order to solve the problem that the model of deep convolutional neural network was complicated and difficult to be applied in the face super-resolution task. The coder composed of rescoding blocks was used for feature extraction, and pyramid reconstruction was introduced into the decoder to achieve fast and accurate super-resolution. To reduce the parameter number of the up-sampling operation in the decoding block, a non-uniform channel widening strategy based on resolution selection was adopted. To avoid adding extra branches, the prior knowledge of the face was introduced through heatmap loss. Experimental results showed that the model proposed in this paper could achieve light and accurate super-resolution reconstruction of ultra-low resolution face images that achieved better visual quality than the state-of-the-art method with lower model complexity.

Key words: deep learning, face super-resolution, asymmetric encoder-decoder, pyramid reconstruction, heatmap loss, generative adversarial networks

CLC Number: 

  • TP391

Fig.1

Network architectures of the AUP-FSRNet"

Fig.2

The structure of basic block"

Fig.3

Feature maps and images with different resolution levels"

Fig.4

The structure of discriminator"

Table 1

The objective evaluation results"

方法 参数量/106 浮点运算量/GFLOPs PSNR/dB SSIM LPIPS NRMSE
Bicubic 23.66 0.637 9 0.557 0 0.113 3
RDN[21] 22.4 6.45 26.79 0.775 0 0.219 5 0.048 6
SRFBN[22] 7.9 279.91 26.92 0.779 1 0.185 4 0.047 9
FSRNet[6] 26.06 0.763 3 0.201 3 0.048 7
FSRGAN[6] 24.70 0.716 8 0.135 3 0.045 2
PFSR[10] 9.0 3.64 24.68 0.687 4 0.105 1 0.046 1
DIC[7] 21.8 14.76 27.15 0.789 6 0.171 1 0.046 0
DICGAN[7] 21.8 14.76 26.05 0.744 3 0.085 5 0.043 8
AUP-FSRNet 4.2 2.81 26.96 0.784 3 0.172 2 0.044 7
AUP-FSRGAN 4.2 2.81 25.87 0.732 6 0.087 4 0.043 1

Fig.5

SR face images reconstructed by different methods"

Fig.6

Models used for ablation experiments"

Table 2

Objective evaluation of ablation experiment"

方法 参数量/106 浮点运算量/GFLOPs PSNR/dB PSNR差值/dB SSIM SSIM差值
AUP-FSRNet 4.2 2.81 25.73 0 0.759 0 0
UP-FSRNet 13.0 23.83 26.03 0.30 0.768 1 0.009 1
P-FSRNet 4.2 7.60 25.92 0.19 0.766 3 0.007 3
AU-FSRNet 4.2 2.81 25.16 -0.57 0.739 0 -0.020 0
AUP-FSRNet(w/o conv1×1) 8.4 2.98 25.56 -0.17 0.750 4 -0.008 6

Fig.7

Subjective evaluation of ablation experiment"

Fig.8

Effect of λheatmap on the performance of AUP-FSRNet"

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