Information-optimal adaptive compressive imaging
2011Vol. 5674, pp. 1255–1259
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Abstract
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.
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