  Taking photographs ''in-the-wild'' is often hindered by fence obstructions
that stand between the camera user and the scene of interest, and which are
hard or impossible to avoid. De-fencing is the algorithmic process of
automatically removing such obstructions from images, revealing the invisible
parts of the scene. While this problem can be formulated as a combination of
fence segmentation and image inpainting, this often leads to implausible
hallucinations of the occluded regions. Existing multi-frame approaches rely on
propagating information to a selected keyframe from its temporal neighbors, but
they are often inefficient and struggle with alignment of severely obstructed
images. In this work we draw inspiration from the video completion literature
and develop a simplified framework for multi-frame de-fencing that computes
high quality flow maps directly from obstructed frames and uses them to
accurately align frames. Our primary focus is efficiency and practicality in a
real-world setting: the input to our algorithm is a short image burst (5
frames) - a data modality commonly available in modern smartphones - and the
output is a single reconstructed keyframe, with the fence removed. Our approach
leverages simple yet effective CNN modules, trained on carefully generated
synthetic data, and outperforms more complicated alternatives real bursts, both
quantitatively and qualitatively, while running real-time.
