Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement

arXiv:2403.02879v1 Announce Type: new
Abstract: Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED. It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains and successfully alleviates the dependence on pairwise training …

abstract application arxiv capabilities cs.cv data diffusion diffusion model image light lighting limitations low novel reference training training data type unsupervised