LFBCNet: Light Field Boundary-aware and Cascaded Interaction Network for Salient Object Detection

In light field imaging techniques, the abundance of stereo spatial information aids in improving the performance of salient object detection. In some complex scenes, however, applying the 4D light field boundary structure to discriminate salient objects from background regions is still under-explored. In this paper, we propose a light field boundary-aware and cascaded interaction network based on light field macro-EPI, named LFBCNet. Firstly, we propose a well-designed light field multi-epipolar-aware learning (LFML) module to learn rich salient boundary cues by perceiving the continuous angle changes from light field macro-EPI. Secondly, to fully excavate the correlation between salient objects and boundaries at different scales, we design multiple light field boundary interactive (LFBI) modules and cascade them to form a light field multi-scale cascade interaction decoder network. Each LFBI is assigned to predict exquisite salient objects and boundaries by interactively transmitting the salient object and boundary features. Meanwhile, the salient boundary features are forced to gradually refine the salient object features during the multi-scale cascade encoding. Furthermore, a light field multi-scale-fusion prediction (LFMP) module is developed to automatically select and integrate multi-scale salient object features for final saliency prediction. The proposed LFBCNet can accurately distinguish tiny differences between salient objects and background regions. Comprehensive experiments on large benchmark datasets prove that the proposed method achieves competitive performance over 2-D, 3-D, and 4-D salient object detection methods.