Exploring contextual priors for real-world image super-resolution

Real-world blind image super-resolution is a challenging problem due to the absence of target high resolution images for training. Inspired by the recent success of the single image generation based method SinGAN, we tackle this challenging problem with a refined model SR-SinGAN, which can learn to perform single real image super-resolution. Firstly, we empirically find that downsampled LR input with an appropriate size can improve the robustness of the generation model. Secondly, we introduce a global contextual prior to provide semantic information. This helps to remove distorted pixels and improve the output fidelity. Finally, we design an image gradient based local contextual prior to guide detail generation. It can alleviate generated artifacts in smooth areas while preserving rich details in densely textured regions (e.g., hair, grass). To evaluate the effectiveness of these contextual priors, we conducted extensive experiments on both artificial and real images. Results show that these priors can stabilize training and preserve output fidelity, improving the generated image quality. We furthermore find that these single image generation based methods work better for images with repeated textures compared to general images.