Restoring Face Occluded Images Using GAN Based Inpainting with Perceptual and Contextual Losses

Restoring face occluded images is a challenging task as missing regions need to be filled based on existing surrounding information. Information extraction from a single image without exploiting context results in degraded quality of images. In this paper, we propose a Deep Convolution Generative Adversarial Network (DCGAN) based inpainting for restoring face occluded images using perceptual and contextual loss functions. The encoder of DCGAN is trained using a combination of context and prior losses. The generator of DCGAN is trained with random latent vector and occluded image encoding from the encoder to generate missing image region. The masking applied during training makes the method robust as the masks are applied at the center of the image, thereby occluding the most informative parts of the image. Experiments performed on CelebA dataset show that DCGAN successfully predicts masked missing regions, outperforming the state-of-the-art methods.