BGI-Net: Bilayer Graph Inference Network for Low Light Image Enhancement

Abstract

In complex industrial environments, enhancing low-light images is crucial for product inspection and fault monitoring. However, current methods often overlook the global structural and local texture similarities in industrial images captured under low-light conditions. To address this issue, we propose a low-light image enhancement framework based on graph convolutional networks (GCNs), i.e., BGI-Net. Considering the common characteristics of industrial images, regions are similar but heavily affected by noise, we designed a denoising module and an enhancement optimization module. The denoising module employs multi-image fusion techniques to efficiently extract information from dimly lit environments. The enhancement optimization module refines images by optimizing graph nodes in both spatial and channel dimensions, leveraging clear image nodes to guide areas with a high signal-to-noise ratio, thereby restoring noise-corrupted details. Extensive qualitative and quantitative evaluations on synthetic and real low-light image datasets demonstrate that our method outperforms state-of-the-art (SoTA) techniques in enhancing the robustness of industrial low-light images.