Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
This work addresses level set estimation for applications like medical imaging and astronomy, but it appears incremental as it builds on existing compressive measurement techniques.
The paper tackles the problem of estimating the level set of a signal from incomplete and noisy compressive measurements, proposing a method based on box-constrained Total Variation regularization and demonstrating its performance relative to state-of-the-art techniques in simulations.
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.