Content area
Light field imaging has been widely acknowledged for its ability to capture both spatial and angular information of a scene, which can improve the performance of salient object detection (SOD) in complex environments. Existing approaches based on refocused images mainly explore the spatial features of different focus areas, while methods based on multi-view images are plagued by limitations such as data redundancy and high computational costs. In this study, we introduce a novel discrete viewpoint selection scheme to mitigate data redundancy. We also leverage the geometric characteristics of light field multi-view images to design a disparity extraction module that extracts disparity-relatedness between the selected viewpoints. Additionally, we construct a multi-feature fusion-feedback module to achieve mutual fusion of multiple features including spatial, edge, and depth for more accurate SOD. To validate our approach, we compare it with 12 existing methods on three datasets, and our results demonstrate a balance between multi-view image redundancy and model performance. Our method accurately locates salient objects even in challenging scenarios such as multiple objects and complex backgrounds, thereby achieving high-precision SOD.