Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
Computer Vision and Pattern Recognition
2012-10-10 v1 Applications
Machine Learning
Abstract
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.
Cite
@article{arxiv.1210.2474,
title = {Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization},
author = {Akshay Soni and Jarvis Haupt},
journal= {arXiv preprint arXiv:1210.2474},
year = {2012}
}