Compressed sensing with side information: Geometrical interpretation and performance bounds
We address the problem of Compressed Sensing (CS) with side information. Namely, when reconstructing a target CS signal, we assume access to a similar signal. This additional knowledge, the side information, is integrated into CS via ℓ<sub>1</sub>-ℓ<sub>1</sub> and ℓ<sub>1</sub>-ℓ<sub>2</sub> minimization. We then provide lower bounds on the number of measurements that these problems require for successful reconstruction of the target signal. If the side information has good quality, the number of measurements is significantly reduced via ℓ<sub>1</sub>-ℓ<sub>1</sub> minimization, but not so much via ℓ<sub>1</sub>-ℓ<sub>2</sub> minimization. We provide geometrical interpretations and experimental results illustrating our findings.