EMNet: Edge-guided multi-level network for salient object detection in low-light images

Abstract

Salient object detection (SOD) has achieved remarkable performance in well-lit scenes. However, when generalized to low-light scenes, the performance of SOD shows significant decrease owing to more challenging conditions such as weak brightness, low contrast, and poor signal-to-noise ratio. To address this issue, we propose a novel edge-guided and multi-level network (EMNet) for SOD in low light images, which learns robust multiscale region features by optimizing the boundaries of salient objects and employing a multi-stage cascaded strategy. To be more specific, the proposed Edge Feature Highlight (EFH) module can establish mapping relationships at different scales and fuse the outputs obtained from pairs of different branches for extracting accurate boundary information. Secondly, Multi-layer Feature Fusion (MFF) module is proposed for combining multi-scale deep features with salient edge cues, using stepwise fusion for effective integration of deep features. Finally, we employ a coarse-to-fine way for iterative prediction to generate high-quality saliency maps. We conducted comprehensive experiments on LLI dataset, and the results demonstrate that the proposed method achieves the best performance in terms of five evaluation metrics, where an average improvement of max Fβ, ωFβ, Em, Sm, and MAE outperforms state-of-the-art method by 7.20%, 12.06%, 4.62%, 5.00%, and 36.34%, respectively.