Recovering filamentary objects in severely degraded binary images using beamlet-driven partitioning

We consider the problem of recovering a binary image consisting of many filaments or linear fragments in the presence of severe binary noise. Our approach exploits beamlets—a dyadically organized, multiscale system of line segments—and associated fast algorithms for beamlet analysis. It considers models based on beamlet-decorated recursive dyadic partitions, and models the image as a Bernoulli random process with spatially variant success probability, which is “high” within the beamlet complexity-penalized model fitting. Simulation results demonstrate the effectiveness of the method.