  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.
