Guided Focal Stack Refinement Network for Light Field Salient Object
  Detection
Bo Yuan Yao Jiang Keren Fu Qijun Zhao
Light field salient object detection (SOD) is an emerging research direction
attributed to the richness of light field data. However, most existing methods
lack effective handling of focal stacks, therefore making the latter involved
in a lot of interfering information and degrade the performance of SOD. To
address this limitation, we propose to utilize multi-modal features to refine
focal stacks in a guided manner, resulting in a novel guided focal stack
refinement network called GFRNet. To this end, we propose a guided refinement
and fusion module (GRFM) to refine focal stacks and aggregate multi-modal
features. In GRFM, all-in-focus (AiF) and depth modalities are utilized to
refine focal stacks separately, leading to two novel sub-modules for different
modalities, namely AiF-based refinement module (ARM) and depth-based refinement
module (DRM). Such refinement modules enhance structural and positional
information of salient objects in focal stacks, and are able to improve SOD
accuracy. Experimental results on four benchmark datasets demonstrate the
superiority of our GFRNet model against 12 state-of-the-art models.