Compressive Image Classification using Deterministic Sensing Matrices

We have studied compressive sensing, where the goal is to capture majority of the information in the signal using very few measurements and obtain accuracte reconstructions. One of the most important aspect of compressive sensing was the design of sensing matrices Φ and we understood that if the sensing matrices obey the Restricted Isometry Property, and obtained worst case performances on the reconstructed signals.