A test-time self-supervised super-resolution method that rescales degradation embeddings by an LPIPS-based quality score and regularizes SR features toward CLIP features improves LPIPS and NRQM on real-world benchmarks.
Low-res leads the way: Improving generalization for super- resolution by self-supervised learning
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High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution
A test-time self-supervised super-resolution method that rescales degradation embeddings by an LPIPS-based quality score and regularizes SR features toward CLIP features improves LPIPS and NRQM on real-world benchmarks.