R2RFlow: Reflectance-to-Restoration Flow with Complementary Evidence for Low Light Image Enhancement

Low-light image enhancement aims to recover visually pleasing normal-light images while preserving the scene content concealed by complex illumination degradation. However, existing generative approaches often start from uninformative noise without directly using the scene content preserved in the low-light observation to initialize the transport. Existing flow objectives primarily supervise the underlying flow field, without directly exploiting the known restoration endpoint available in paired restoration. To address these limitations, we propose R2RFlow, a reflectance-to-restoration flow framework integrating complementary evidence for one-step low-light image enhancement. R2RFlow initializes the transport from an observation-derived reflectance state and constructs finite-interval transport toward the paired normal-light image, explicitly leveraging scene content available in the degraded observation. To complement restoration-relevant information not fully retained by the reflectance source, we further incorporate structural, illumination, and frequency cues through role-specific conditioning pathways. In addition, a restoration-aligned multi-path learning strategy supervises the shared transport operator from the deployment source, reference intermediate states, and model-induced rollout states through their final restoration outcomes. Experiments on the LOL-v1 and LOL-v2-Real benchmarks show that R2RFlow achieves competitive performance against existing low-light enhancement methods. When trained exclusively on LOL-v1, R2RFlow also achieves favorable performance on LSRW-Nikon and LSRW-Huawei without target-domain fine-tuning, demonstrating favorable cross-dataset transfer.