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Synthetic apertures find applications in many fields, such as radar, radiotelescopes, microscopy, sonar, ultrasound, LiDAR, and optical imaging. Theyapproximate the signal of a single hypothetical wide aperture sensor witheither an array of static small aperture sensors or a single moving smallaperture sensor. Common sense in synthetic aperture sampling is that a densesampling pattern within a wide aperture is required to reconstruct a clearsignal. In this article we show that there exists practical limits to both,synthetic aperture size and number of samples for the application of occlusionremoval. This leads to an understanding on how to design synthetic aperturesampling patterns and sensors in a most optimal and practically efficient way.We apply our findings to airborne optical sectioning which uses camera dronesand synthetic aperture imaging to computationally remove occluding vegetationor trees for inspecting ground surfaces.

A Statistical View on Synthetic Aperture Imaging for Occlusion Removal