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SRGAN with Total Variation Loss in Face Super-Resolution

机译：SRAGAN面部超级分辨率的总变异损失

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摘要

Facial image super-resolution is a crucial preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Convolutional neural networks were earlier used to produce high-resolution images that train quicker and shown excellent performance by learning mapping relation using pairs of low-resolution and high-resolution images. However, in some cases, they are incapable of recovering finer details and often generate blurry images. In this paper, we evaluate a method of applying Generative adversarial networks in generating realistic super-resolution images from low-resolution ones by using three typical losses for super-resolution: Content Loss, Adversarial Loss, Perceptual Loss, and proposed to use Total Variation Loss. We try different pre-trained famous Convolutional neural networks models (VGG19, FaceNet, and EfficientNet) in Perceptual Loss to have a general view with different backbones. Our network gains 32.67 of Peak signal-to-noise ratio (PSNR) and 0.89 of Structural similarity index (SSIM) in 100 random samples from the Flickr-Faces-HQ dataset.
机译：面部图像超分辨率是面部图像分析，面部识别和基于图像的3D面重建的关键预处理。卷积神经网络之前用于生产快速培训的高分辨率图像，并通过使用对低分辨率和高分辨率图像的映射关系来实现出色的性能。然而，在某些情况下，它们无法恢复更细的细节并经常产生模糊图像。在本文中，我们通过使用三种典型的超分辨率损失来评估一种应用生成的对冲网络，以便通过三种典型的超分辨率损失：内容丢失，对抗丧失，感知损失，并提出使用总体变化损失。我们尝试在感知损失中尝试不同的预先训练的着名卷积神经网络模型（VGG19，Faceget，Abseralnet），以具有不同骨干的一般视图。我们在FlickR-Faces-HQ数据集中获得了100个无随机样本的32.67峰值信噪比（PSNR）和0.89的结构相似性指数（SSIM）。

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