Degradation regression with uncertainty for blind super-resolution

Abstract

Some recent blind super-resolution (SR) efforts focus on designing complex degradation models to better simulate real-world degradations. The paired high-resolution (HR) & low-resolution (LR) samples synthesized by these models can cover a large degradation space, which helps train a robust SR model in real scenarios. However, these diverse synthetic samples may render the SR model degradation-unaware and prevent it from achieving optimal results on LR images with specific degradations. Alternatively, another category of methods is proposed to estimate specific degradations in the given application and then tailor a degradation-aware SR model accordingly. Nonetheless, degradation estimation is an ill-posed problem and accurate estimation is quite challenging. Towards these issues, we propose a probabilistic degradation estimator (PDE) which can predict the degradation as a certain distribution rather than a single point. Specifically, we develop an intersection over union (IoU) based degradation regression loss with uncertainty, which could lead PDE to shrink the possible degradation space of the test LR image. This enables the degradation model to synthesize more degradation-specific training samples and further improve SR performance. In this way, our PDE can alleviate degradation redundancy compared with degradation-unaware methods and is more robust to the degradation estimation error than previous degradation-aware methods. Extensive experiments show that the proposed PDE can help the SR model produce better results on both synthetic and real-world images.