Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
For image super-resolution (SR) bridging the gap between the performance on synthetic
datasets and real-world degradation scenarios remains a challenge. This work introduces a …
datasets and real-world degradation scenarios remains a challenge. This work introduces a …
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
H Chen, W Li, J Gu, J Ren, H Sun, X Zou… - arXiv preprint arXiv …, 2024 - arxiv.org
For image super-resolution (SR), bridging the gap between the performance on synthetic
datasets and real-world degradation scenarios remains a challenge. This work introduces a …
datasets and real-world degradation scenarios remains a challenge. This work introduces a …
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
H Chen, W Li, J Gu, J Ren, H Sun, X Zou… - arXiv e …, 2024 - ui.adsabs.harvard.edu
For image super-resolution (SR), bridging the gap between the performance on synthetic
datasets and real-world degradation scenarios remains a challenge. This work introduces a …
datasets and real-world degradation scenarios remains a challenge. This work introduces a …