DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Data

Conventional RGB cameras suffer from lighting artifacts suchas flare, glare, flicker, and overexposure, leading to irrecoverable informa-tion loss that necessitates computational restoration. However, existingapproaches treat these problems in isolation, failing to recover structuraldetails completely obscured by complex spatially discrete image degrada-tions. In this paper, we propose a novel cross-modal restoration paradigmand present DeLux, a modular proof-of-concept pipeline that leveragesneuromorphic event streams as a structural prior to guide the targeteddetection and inpainting of lighting artifacts in RGB video. Validation onsynthetic benchmarks and real-world automotive footage demonstratesthat DeLux effectively suppresses local artifacts and restores affected re-gions. The proposed approach outperforms existing RGB-only baselinesand event-guided HDR models, achieving an average MS-SSIM of over0.99 across all artifact types and demonstrating up to an 88% reductionin artifact severity in real-world automotive footage. The synthetic arti-fact generation tools and curated real-world evaluation datasets are madepublicly available to foster future research on cross-modal restoration.