Abstract
In this paper, we propose a structured image inpainting method employing an energy based model. In order to learn structural relationship between patterns observed in images and missing regions of the images, we employ an energy-based structured prediction method. The structural relationship is learned by minimizing an energy function which is defined by a simple convolutional neural network. The experimental results on various benchmark datasets show that our proposed method significantly outperforms the state-of-the-art methods which use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively. The code is publicly available.
Abstract (translated)
在本文中，我们提出了一种采用基于能量的模型的结构化图像修复方法。为了学习图像中观察到的图案与图像缺失区域之间的结构关系，我们采用了基于能量的结构化预测方法。通过最小化由简单卷积神经网络定义的能量函数来学习结构关系。各种基准数据集的实验结果表明，我们提出的方法明显优于使用生成对抗网络（GAN）的最先进方法。我们在Olivetti人脸数据集上获得了497.35均方误差（MSE），而最先进的方法提供了833.0 MSE。此外，我们在SVHN数据集上获得了28.4 dB峰值信噪比（PSNR），在CelebA数据集上获得了23.53 dB，相比之下，分别由最先进的方法提供了22.3 dB和21.3 dB。该代码是公开的。
URL
https://arxiv.org/abs/1801.07939
