EcDiff-LLIE: Event-conditional diffusion model for structure-preserving low-light image enhancement

Abstract

Low-light image enhancement (LLIE) aims to restore the visual quality of poorly illuminated images by recovering fine details and textures while suppressing noise and artifacts. Recently, diffusion models have shown superior generative capabilities for LLIE. However, existing diffusion-based methods condition the denoising process only on low-light images or features derived from them (e.g., structural or illumination maps). Since the low-light images are severely degraded, this limits the denoising model’s ability to restore fine structure and reduce artifacts. In this work, we show that the event data captured simultaneously with the low-light images provides complementary high-dynamic-range and high-temporal-resolution structural information that can overcome this limitation. Therefore, we propose EcDiff-LLIE, a novel event-conditional diffusion framework for LLIE. At its core, we introduce a multimodality denoising network that conditions on both low-light images and concurrent event streams. To effectively fuse the two modalities, we design a cross-modality attention block that bridge their domain differences, while also enabling long-range dependency modeling for improved structural preservation. Experiments on the synthetic SDSD and real-world SDE datasets show significant improvements in quantitative evaluation metrics. Furthermore, evaluation on the high-resolution real-world HUE dataset further shows the generalization ability of the proposed framework.