The paper "Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning" introduces a novel training framework called "Low-Res Leads the Way" (LWay) to enhance the adaptability of super-resolution (SR) models to real-world images. LWay combines supervised pre-training with self-supervised learning, utilizing a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images. These embeddings are then merged with super-resolved outputs for LR reconstruction. The approach leverages unseen LR images for self-supervised learning, guiding 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 LWay significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets, outperforming existing methods. The training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications. The paper discusses the limitations and comparisons with other methods, emphasizing the robustness and effectiveness of LWay in handling complex and variable degradation patterns.The paper "Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning" introduces a novel training framework called "Low-Res Leads the Way" (LWay) to enhance the adaptability of super-resolution (SR) models to real-world images. LWay combines supervised pre-training with self-supervised learning, utilizing a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images. These embeddings are then merged with super-resolved outputs for LR reconstruction. The approach leverages unseen LR images for self-supervised learning, guiding 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 LWay significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets, outperforming existing methods. The training regime is universally compatible, requiring no network architecture modifications, making it a practical solution for real-world SR applications. The paper discusses the limitations and comparisons with other methods, emphasizing the robustness and effectiveness of LWay in handling complex and variable degradation patterns.