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

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

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

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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