Continuous Degradation Modeling via Latent Flow Matching for Real-World Super-Resolution

• URL: http://arxiv.org/abs/2602.04193v1
• Date: Wed, 04 Feb 2026 04:16:38 GMT
• Title: Continuous Degradation Modeling via Latent Flow Matching for Real-World Super-Resolution
• Authors: Hyeonjae Kim, Dongjin Kim, Eugene Jin, Tae Hyun Kim,
• Abstract summary: We introduce a novel framework capable of synthesizing authentic LR images from a single HR image by leveraging the latent degradation space with flow matching.<n>Our approach generates LR images with realistic artifacts at unseen degradation levels, which facilitates the creation of large-scale, real-world SR training datasets.
• Score: 11.776067915986687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning-based super-resolution (SR) methods have shown impressive outcomes with synthetic degradation scenarios such as bicubic downsampling, they frequently struggle to perform well on real-world images that feature complex, nonlinear degradations like noise, blur, and compression artifacts. Recent efforts to address this issue have involved the painstaking compilation of real low-resolution (LR) and high-resolution (HR) image pairs, usually limited to several specific downscaling factors. To address these challenges, our work introduces a novel framework capable of synthesizing authentic LR images from a single HR image by leveraging the latent degradation space with flow matching. Our approach generates LR images with realistic artifacts at unseen degradation levels, which facilitates the creation of large-scale, real-world SR training datasets. Comprehensive quantitative and qualitative assessments verify that our synthetic LR images accurately replicate real-world degradations. Furthermore, both traditional and arbitrary-scale SR models trained using our datasets consistently yield much better HR outcomes.

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