  The extraction of a clean background image by removing foreground occlusion
holds immense practical significance, but it also presents several challenges.
Presently, the majority of de-occlusion research focuses on addressing this
issue through the extraction and synthesis of discrete images from calibrated
camera arrays. Nonetheless, the restoration quality tends to suffer when faced
with dense occlusions or high-speed motions due to limited perspectives and
motion blur. To successfully remove dense foreground occlusion, an effective
multi-view visual information integration approach is required. Introducing the
spike camera as a novel type of neuromorphic sensor offers promising
capabilities with its ultra-high temporal resolution and high dynamic range. In
this paper, we propose an innovative solution for tackling the de-occlusion
problem through continuous multi-view imaging using only one spike camera
without any prior knowledge of camera intrinsic parameters and camera poses. By
rapidly moving the spike camera, we continually capture the dense stream of
spikes from the occluded scene. To process the spikes, we build a novel model
\textbf{SpkOccNet}, in which we integrate information of spikes from continuous
viewpoints within multi-windows, and propose a novel cross-view mutual
attention mechanism for effective fusion and refinement. In addition, we
contribute the first real-world spike-based dataset \textbf{S-OCC} for
occlusion removal. The experimental results demonstrate that our proposed model
efficiently removes dense occlusions in diverse scenes while exhibiting strong
generalization.
