Salient object detection in low-light images via functional optimization-inspired feature polishing

Abstract

Salient object detection under low-light conditions remains a challenge in many practical applications. Most recent works focus on feature integration without considering filtering out clutter from the darkness, thus limiting the detection performance. To tackle this issue, we propose a low-light image salient object detection network (LLISOD), which generates highly accurate saliency maps by functional optimization-inspired feature polishing strategy. The LLISOD includes: (1) An unfolded implicit nonlinear mapping (UINM) module uniquely designed for polishing feature maps; and (2) the hierarchical feature polishing (HFP) streams proposed for fusing the outputs of the UINM module on the top-down pathway to refine the saliency predictions. Furthermore, we provide a new dataset for benchmarking the investigation of salient object detection in low-light images. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. Code will be available at https://github.com/yuehuihui000/LLISOD.