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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Face Hallucination via Convolution Neural Network
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Face Hallucination via Convolution Neural Network

机译：通过卷积神经网络进行人脸幻觉

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摘要

Deep learning methods have been successfully used in many areas of computer vision, including super resolution. However, all of the previous deep learning methods have been proposed for generic image super resolution. In this paper, we proposed to use convolutional neural network for face hallucination (FH) by combining the domain specific prior knowledge of face images and properties of deep learning. In the proposed method, an end to end mapping is learned as a deep convolutional network between the low resolution (LR) images and their corresponding high resolution (HR) images to upscale the input face image directly. In order to achieve larger magnification factor, we consider to cascade several convolution neural networks each of which is with a fixed up-scaling factor and upscales the LR image step by step. Experimental result shows that our proposed method can achieve better performance comparing to the traditional face hallucination methods.
机译：深度学习方法已成功用于计算机视觉的许多领域，包括超分辨率。但是，已经提出了所有以前的深度学习方法用于通用图像超分辨率。在本文中，我们提出将卷积神经网络用于人脸幻觉（FH），方法是结合特定于领域的人脸图像先验知识和深度学习属性。在提出的方法中，端到端映射被学习为低分辨率（LR）图像与其对应的高分辨率（HR）图像之间的深度卷积网络，以直接放大输入的人脸图像。为了获得更大的放大倍数，我们考虑级联几个卷积神经网络，每个卷积神经网络都具有固定的放大系数，并逐步放大LR图像。实验结果表明，与传统的面部幻觉方法相比，本文提出的方法具有更好的性能。

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