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

• URL: http://arxiv.org/abs/2403.02601v1
• Date: Tue, 5 Mar 2024 02:29:18 GMT
• Title: Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
• Authors: Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Haoze Sun, Xueyi Zou, Zhensong Zhang, Youliang Yan, Lei Zhu
• Abstract summary: This work introduces a novel "Low-Res Leads the Way" (LWay) training framework 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 reconstruction. Our training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications.
• Score: 45.13580581290495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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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