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Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning

Haoyu Chen · Wenbo Li · Jinjin Gu · Jingjing Ren · Haoze Sun · Xueyi Zou · Youliang Yan · Zhensong Zhang · Lei Zhu

Arch 4A-E Poster #166
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Fri 21 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 novel "Low-Res Leads the Way" (LWay) training framework, merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images, merging them with super-resolved outputs for LR image reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain, facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets, outperforming existing methods. Our training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications.

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