Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

🤖 Plain-English Summary

Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded images. Extensive comparisons have shown its superior visual performance than prior works on various real datasets.

🔑 Key Findings

• In this work, we extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.
• Specifically, a high-order degradation modeling process is introduced to better simulate complex real-world degradations.
• We also consider the common ringing and overshoot artifacts in the synthesis process.

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