  Support estimation (SE) of a sparse signal refers to finding the location
indices of the non-zero elements in a sparse representation. Most of the
traditional approaches dealing with SE problem are iterative algorithms based
on greedy methods or optimization techniques. Indeed, a vast majority of them
use sparse signal recovery techniques to obtain support sets instead of
directly mapping the non-zero locations from denser measurements (e.g.,
Compressively Sensed Measurements). This study proposes a novel approach for
learning such a mapping from a training set. To accomplish this objective, the
Convolutional Support Estimator Networks (CSENs), each with a compact
configuration, are designed. The proposed CSEN can be a crucial tool for the
following scenarios: (i) Real-time and low-cost support estimation can be
applied in any mobile and low-power edge device for anomaly localization,
simultaneous face recognition, etc. (ii) CSEN's output can directly be used as
"prior information" which improves the performance of sparse signal recovery
algorithms. The results over the benchmark datasets show that state-of-the-art
performance levels can be achieved by the proposed approach with a
significantly reduced computational complexity.
