AI RESEARCH
DINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integration
arXiv CS.CV
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ArXi:2603.12579v1 Announce Type: new This paper presents a new ambient light normalization framework, DINOLight, that integrates the self-supervised model DINOv2's image understanding capability into the restoration process as a visual prior. Ambient light normalization aims to re images degraded by non-uniform shadows and lighting caused by multiple light sources and complex scene geometries. We observe that DINOv2 can reliably extract both semantic and geometric information from a degraded image.