Identifiability and Noise Robustness for l1-Analysis Regularizations in Compressive Sensing

Abstract

We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.