  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.
