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首页> 外文会议>Cross Strait Radio Science and Wireless Technology Conference >Face Beauty: Improving Quality of Face with Semantic Segmentation Prior and Style Encoder
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Face Beauty: Improving Quality of Face with Semantic Segmentation Prior and Style Encoder

机译：面孔美：用语义细分的面部提高面部的质量和风格的编码器

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摘要

Despite image super-resolution has great progress in recent years, state-of-the-art face super-resolution still has much potential to promote visual quality. Most of these methods utilize a deep convolutional neural network to explore a mapping between low resolution and high resolution, but it cannot well explore facial structures and local knowledge. In this work, we propose a face hallucination method that explicitly incorporates semantic segmentation prior and style encoder to improve the quality of low resolution face images. To enhance the feature mapping and color mapping of the face, we focus on transferring the prior information extracted from the segmentation mask to the super-resolution process. Furthermore, we add the color attention residual block as a color fidelity unit to preserve the color information of the mapped area. In this way, we can input the latent code generated by the style encoder as parameters into the network to improve the image quality. Experimental results demonstrate that our proposed model achieves superior performance over state-of-the-art approaches including the enhanced super-resolution generative adversarial networks (ESRGAN) and Residual Channel Attention Networks (RCAN).
机译：尽管图像超级分辨率近年来，但最先进的面部超级分辨率仍然有可能促进视觉质量。这些方法中的大多数利用深度卷积神经网络来探索低分辨率和高分辨率之间的映射，但无法探索面部结构和本地知识。在这项工作中，我们提出了一种脸部幻觉方法，明确地融合了语义分割现有和风格编码器，以提高低分辨率面部图像的质量。为了增强面部的特征映射和颜色映射，我们专注于将从分段掩码提取的先前信息传输到超分辨率的过程。此外，我们将颜色注意力块添加为颜色保真单元，以保留映射区域的颜色信息。通过这种方式，我们可以输入样式编码器生成的潜在代码作为进入网络的参数，以提高图像质量。实验结果表明，我们的拟议模型实现了卓越的现有方法，包括增强的超分辨率生成对冲网络（ESRAN）和残余通道注意网络（RCAN）。

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