Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
This work addresses the problem of poor generalization in super-resolution models for real-world applications, offering a practical solution without requiring network modifications.
The paper tackles the challenge of improving generalization for image super-resolution in real-world degradation scenarios by introducing a self-supervised learning framework that adapts models to unseen low-resolution images, resulting in significant improvements in generalization and detail restoration capabilities compared to existing methods.
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.