Unsupervised real-world image super-resolution via rectified flow degradation modeling

Unsupervised real-world image super-resolution (SR) faces critical challenges due to the complex, unknown degradation distributions in practical scenarios. Due to a significant domain gap, existing methods struggle to generalize from synthetic low-resolution (LR) and high-resolution (HR) image pairs to real-world data. In this paper, we propose an unsupervised real-world SR method based on rectified flow to capture and model real-world degradation effectively, synthesizing LR–HR training pairs with realistic degradation. Specifically, given unpaired LR and HR images, we propose a novel rectified flow degradation module (RFDM) that introduces degradation-transformed LR (DT-LR) images as intermediaries. By modeling the degradation trajectory continuously and invertibly, RFDM better captures real-world degradation and enhances the realism of generated LR images. Additionally, we propose a Fourier prior guided degradation module (FGDM) that leverages structural information embedded in Fourier phase components to ensure precise modeling of real-world degradation. Finally, the LR images are processed by both FGDM and RFDM, producing final synthetic LR images with real-world degradation. The synthetic LR images are paired with given HR images to train off-the-shelf SR networks. Extensive experiments on real-world datasets demonstrate that our method significantly improves the performance of off-the-shelf approaches in real-world scenarios.