Model-guided Adaptive Recovery of Compressive Sensing

For the new signal acquisition methodology of compressive sensing (CS) a challenge is to find a space in which the signal is sparse and hence recoverable faithfully. Given the nonstationarity of many natural signals such as images, the sparse space is varying in time or spatial domain. As such, CS recovery should be conducted in locally adaptive, signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of …