Information-optimal adaptive compressive imaging

We adopt a sequential Bayesian experiment design framework for compressive imaging wherein the measurement basis is data dependent and therefore adaptive. The criteria for measurement basis design employs the task-specific information (TSI), an information theoretic metric, that is conditioned on the past measurements. A Gaussian scale mixture prior model is used to represent compressible natural scenes in theWavelet basis. The resulting adaptive compressive imager design yields significant performance improvements compared to a static compressive imager using random projections.