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A super resolution frontal face generation model based on 3DDFA and CBAM
Displays ( IF 4.3 ) Pub Date : 2021-06-30 , DOI: 10.1016/j.displa.2021.102043
Chuanming Niu , Fangzhe Nan , Xinran Wang

Pose and low resolution seriously affect the synthesis of high-quality frontal face images. With the development of deep learning, a large number of models based on the deep neural network are used to solve the problem of face pose and image super-resolution. However, the synthesis of the high-resolution frontal face is still a problem that has not been fully studied. Therefore, in this paper, we propose a method to realize image super-resolution and face frontal generation simultaneously. Specifically, we propose a frontal face model FFSR_GAN used to generate super-resolution. This model mainly solves the problem of low resolution and large face pose. There are two main improvements: 1) Aiming at the problem of artifacts in the image generated by the face frontal generation module, the face frontal generation module is designed based on 3DDFA and CBAM; 2) Aiming at the problem of low resolution in frontal face generation, a face super-resolution module is carefully designed, which is used for super-resolution of the generated frontal face. The method proposed in this paper solves the problem of face pose and super-resolution for the first time and improves the recognition accuracy of low-resolution and face images with larger posture. The experimental results on the existing public dataset prove the advantages of the FFSR_GAN model.



中文翻译:

基于3DDFA和CBAM的超分辨率人脸生成模型

姿势和低分辨率严重影响高质量正面人脸图像的合成。随着深度学习的发展,大量基于深度神经网络的模型被用于解决人脸姿态和图像超分辨率问题。然而,高分辨率正面人脸的合成仍然是一个尚未得到充分研究的问题。因此,在本文中,我们提出了一种同时实现图像超分辨率和面部正面生成的方法。具体来说,我们提出了一种用于生成超分辨率的正面人脸模型 FFSR_GAN。该模型主要解决低分辨率和大人脸姿态的问题。主要有两个改进:1)针对人脸正面生成模块生成的图像中存在伪影的问题,基于3DDFA和CBAM设计人脸生成模块;2) 针对人脸生成分辨率低的问题,精心设计了人脸超分辨率模块,用于对生成的人脸进行超分辨率。本文提出的方法首次解决了人脸姿态和超分辨率问题,提高了低分辨率和较大姿态人脸图像的识别精度。在现有公共数据集上的实验结果证明了 FFSR_GAN 模型的优势。本文提出的方法首次解决了人脸姿态和超分辨率问题,提高了低分辨率和较大姿态人脸图像的识别精度。在现有公共数据集上的实验结果证明了 FFSR_GAN 模型的优势。本文提出的方法首次解决了人脸姿态和超分辨率问题,提高了低分辨率和较大姿态人脸图像的识别精度。在现有公共数据集上的实验结果证明了 FFSR_GAN 模型的优势。

更新日期:2021-07-04
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