基于非对称U型金字塔重建的轻量级人脸超分辨率网络

doi:10.6040/j.issn.1672-3961.0.2021.312

摘要/Abstract

摘要：

为解决深度卷积神经网络在人脸超分辨率任务中模型复杂并难以实际应用的问题, 提出一种轻量级人脸超分辨率网络。利用残差编码块构成的编码结构进行特征提取, 在解码结构中引入金字塔重建从而实现快速准确的超分辨率。为降低解码块中上采样操作的参数量, 采用基于分辨率选择的非一致通道扩宽策略。为避免增加分支, 通过热图损失引入人脸先验知识。试验结果表明, 本研究提出的模型轻量有效地实现了超低分辨率人脸图像的超分辨重建, 以较低的模型复杂度, 重建出视觉质量优于其他先进方法的超分辨率人脸图像。

关键词: 深度学习, 人脸超分辨率, 非对称编解码, 金字塔重建, 热图损失, 生成对抗网络

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

中图分类号:

TP391

蒋桐雨, 陈帆, 和红杰. 基于非对称U型金字塔重建的轻量级人脸超分辨率网络[J]. 山东大学学报 (工学版), 2022, 52(1): 1-8.

Tongyu JIANG, Fan CHEN, Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction[J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8.

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参考文献 22

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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

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

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