Unsupervised Real-World Super-Resolution via Rectified Flow Degradation Modelling

• URL: http://arxiv.org/abs/2508.07214v1
• Date: Sun, 10 Aug 2025 07:27:28 GMT
• Title: Unsupervised Real-World Super-Resolution via Rectified Flow Degradation Modelling
• Authors: Hongyang Zhou, Xiaobin Zhu, Liuling Chen, Junyi He, Jingyan Qin, Xu-Cheng Yin, Zhang xiaoxing,
• Abstract summary: Unsupervised real-world super-resolution (SR) faces critical challenges due to the complex, unknown degradation distributions.<n>Existing methods struggle to generalize from synthetic low-resolution (LR) and high-resolution (HR) image pairs to real-world data.<n>We propose an unsupervised real-world SR method based on rectified flow to effectively capture and model real-world degradation.
• Score: 16.485914675883972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised real-world super-resolution (SR) faces critical challenges due to the complex, unknown degradation distributions in practical scenarios. Existing methods struggle to generalize from synthetic low-resolution (LR) and high-resolution (HR) image pairs to real-world data due to a significant domain gap. In this paper, we propose an unsupervised real-world SR method based on rectified flow to effectively capture and model real-world degradation, 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 in a continuous and invertible manner, 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 more 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 the given HR images to train the off-the-shelf SR networks. Extensive experiments on real-world datasets demonstrate that our method significantly enhances the performance of existing SR approaches in real-world scenarios.

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