Implementation of Compressed Sensing algorithms in Python

Abstract

Compressed Sensing (CS) is an emerging sampling paradigm that has recently proved to be an effective approach to polarimetric SAR tomography. This work focuses on the practical implementation of CS reconstruction algorithms via convex optimization. Specifically, we used the Python programming language and implemented a second order cone program (SOCP) that deals with multiple looks as well as multiple polarizations simultaneously. Also, special consideration was given to handling complex data appropriately. Finally, the methods will be validated by using airborne data acquired by the E-SAR sensor of DLR.