Reconstructing Missing Areas in Facial Images

Christoph Jansen, Radek Mackowiak, N. Hezel, Moritz Ufer, Gregor Altstadt, K. U. Barthel
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引用次数: 2

Abstract

In this paper, we present a novel approach to reconstruct missing areas in facial images by using a series of Restricted Boltzman Machines (RBMs). RBMs created with a low number of hidden neurons generalize well and are able to reconstruct basic structures in the missing areas. On the other hand networks with many hidden neurons tend to emphasize details, when using the reconstruction of the previous, more generalized RBMs, as their input. Since trained RBMs are fast in encoding and decoding data by design, our method is also suitable for processing video streams.
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人脸图像缺失区域的重建
本文提出了一种利用一系列受限玻尔兹曼机(rbm)重建人脸图像缺失区域的新方法。隐藏神经元数量少的rbm泛化效果好,能够重建缺失区域的基本结构。另一方面,当使用先前更广义的rbm的重建作为输入时,具有许多隐藏神经元的网络倾向于强调细节。由于经过训练的rbm在编码和解码数据的设计上是快速的,因此我们的方法也适用于视频流的处理。
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