  Estimating the level set of a signal from measurements is a task that arises
in a variety of fields, including medical imaging, astronomy, and digital
elevation mapping. Motivated by scenarios where accurate and complete
measurements of the signal may not available, we examine here a simple
procedure for estimating the level set of a signal from highly incomplete
measurements, which may additionally be corrupted by additive noise. The
proposed procedure is based on box-constrained Total Variation (TV)
regularization. We demonstrate the performance of our approach, relative to
existing state-of-the-art techniques for level set estimation from compressive
measurements, via several simulation examples.
