HLGNet: High-Light Guided Network for low-light instance segmentation with spatial-frequency domain enhancement

Abstract

Instance segmentation models generally perform well under typical lighting conditions but struggle in low-light environments due to insufficient fine-grained detail. To address this, frequency domain enhancement has shown promise. However, the lack of spatial domain processing in existing frequency domain based methods often results in poor boundary delineation and inadequate local perception. To address these challenges, we propose HLGNet (High-Light Guided Network). By leveraging high-light image masks, our approach integrates enhancements in both the frequency and spatial domains, thereby improving the feature representation of low-light images. Specifically, we propose the SPE (Spatial-Frequency Enhancement) Block, which effectively combines and complements local spatial features with global frequency domain information. Additionally, we design the DAF (Dynamic Affine Fusion) module to inject frequency domain information into semantically significant features, thereby enhancing the model’s ability to capture both detailed target information and global semantic context. Finally, we propose the HLG Decoder, which dynamically adjusts the attention distribution by utilizing mutual information and entropy, guided by high-light image masks. This ensures improved focus on both local details and global semantics. Extensive quantitative and qualitative evaluations on two widely used low-light instance segmentation datasets demonstrate that HLGNet outperforms current state-of-the-art methods.