DRGAN: A dual resolution guided low-resolution image inpainting

Although image inpainting is a challenging task in computer vision, most existing image inpainting methods have achieved remarkable progress. However, occlusion and low resolution often appear on one image simultaneously in the real world, and only a few methods can address low-resolution image inpainting. To tackle this problem, we propose a novel method called the Dual Resolution Generative Adversarial Network (DRGAN), which formulates low-resolution (LR) image inpainting as a constrained image generation problem. The proposed DRGAN first trains a generative network to obtain high-quality images from noise vectors. Then a dual-resolution loss is designed to effectively integrate both high- and low-resolution information to optimize the input vectors initialized by random noise to generate high-quality images. Compared with the model that learns the mapping function from low- to high-quality images, the proposed model maps input noise vectors to images and can thus adapt more effectively to various types of degradation and large occlusions. Extensive experiments on synthetic LR data and real-world images demonstrate the effectiveness of the proposed DRGAN.