Degradation-Guided Meta-Restoration Network for Blind Super-Resolution

Understand

Blind super-resolution (SR) aims to recover high-quality visual textures from a low-resolution (LR) image, which is usually degraded by down-sampling blur kernels and additive noises.

• This task is extremely difficult due to the challenges of complicated image degradations in the real-world.
• Existing SR approaches either assume a predefined blur kernel or a fixed noise, which limits these approaches in challenging cases.
• In this paper, we propose a Degradation-guided Meta-restoration network for blind Super-Resolution (DMSR) that facilitates image restoration for real cases.