Title: DGMS: Domain Generalization Method for Mamba-based Super-Resolution Networks
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Domain generalization, Super resolution

Abstract: Mamba-based domain generalization methods can effectively improve the performance of Mamba networks on samples with unknown distributions. However, existing methods target high-level  vision tasks like image and point cloud classification, with limited research on low-level vision tasks such as image super-resolution (SR). To bridge this gap, we propose a Domain Generalization method for Mamba-based Super-Resolution networks (DGMS), which introduces a domain shift metric for SR tasks and identifies key variables governing domain shifts. Subsequently, based on the identified key variables, we propose hidden state update regularization and parameter consistency regularization. Through explicit supervisory constraints on these key variables, the method effectively enhances the network’s generalization performance across images with different degradation models. Extensive experiments across diverse data distributions and network architectures demonstrate the effectiveness of the proposed method on low-level vision tasks, where it outperforms existing state-of-the-art Mamba-based domain generalization methods.
Our code is available at ***.

TL;DR: We propose a domain generalization method for Mamba-based SR networks (DGMS), which effectively enhances the performance of Mamba-based SR networks on target domain samples with unknown distributions after training with source domain samples.