基于改进SRGAN模型的人脸图像超分辨率重建

doi:10.19678/j.issn.1000-3428.0064174

摘要/Abstract

摘要： 传统生成对抗网络模型重建人脸图像时出现过多失真，难以在减少失真的情况下有效提高人脸图像真实感。针对该问题，在生成对抗网络SRGAN模型的基础上，提出一种改进的人脸图像超分辨率重建方法。为提高重建像素点与周围像素点的相关性，将双注意力机制模块嵌入到SRGAN模型的生成器和判别器中，在空间域和通道域中获取更精准的特征依赖关系。同时应用自适应激活函数ACON取代原SRGAN网络中的激活函数，通过动态学习ACON激活函数参数为每个神经元设计不同激活形式，从而提高网络特征表达能力。使用改进SRGAN的人脸图像超分辨率重建算法在CelebA测试集上进行重建实验，结果表明：该算法较原算法PSNR值提高0.675 dB，SSIM值提高0.016，LPIPS值优化0.036，有效减少了重建人脸图像中眼睛等重点部位的失真情况；与其他非生成对抗网络的主流算法相比，LPIPS值最低优化0.107，最高优化0.205，有效提高了重建人脸图像的真实感。

关键词: 超分辨率重建, 生成对抗网络, 注意力机制, 自适应激活函数, 特征提取

Abstract: Because the traditional generation countermeasure network model exhibits excessive distortion when reconstructing the face image, it is difficult to effectively improve the realism of the face image.Under the condition of reducing the distortion, an improved super-resolution reconstruction method of face images based on SRGAN model is proposed.To increase the correlation between the reconstructed and surrounding pixels, the Dual Attention(DA) mechanism module is embedded in the generator and discriminator of the SRGAN model to obtain more accurate feature dependency in the spatial and channel domains.An adaptive activation function, termed ACON, is used to replace the original activation function in an SRGAN network, and different activation forms are designed for each neuron by dynamically learning the parameters of the ACON activation function, to improve the ability of network feature expression.The reconstruction experiment is performed on the CelebA using the improved SRGAN face image reconstruction algorithm.The results show that compared with the original algorithm, the PSNR value increases by 0.675 dB, the SSIM value increases by 0.016, and the LPIPS value is optimized by 0.036, effectively reducing the distortion of key parts, such as the eyes, in the reconstructed face image.Compared with other mainstream algorithms that do not generate countermeasure networks, the LPIPS value is optimized by 0.107 at the lowest and 0.205 at the highest, effectively improving the realism of the reconstructed face image.

Key words: super-resolution reconstruction, generative adversarial network, attention mechanism, adaptive activation function, feature extraction

中图分类号:

TP391

李培育, 张雅丽. 基于改进SRGAN模型的人脸图像超分辨率重建[J]. 计算机工程, 2023, 49(4): 199-205.

LI Peiyu, ZHANG Yali. Face Image Super-Resolution Reconstruction Based on Improved SRGAN Model[J]. Computer Engineering, 2023, 49(4): 199-205.

https://www.ecice06.com/CN/Y2023/V49/I4/199

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