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Compressive dynamic range imaging via Bayesian shrinkage dictionary learning

Abstract. We apply the Bayesian shrinkage dictionary learning into compressive dynamic-range imaging. By attenuating the luminous intensity impinging upon the detector at the pixel level, we demonstrate a conceptual design of an 8-bit camera to sample high-dynamic-range scenes with a single snapshot. Coding strategies for both monochrome and color cameras are proposed. A Bayesian reconstruction algorithm is developed to learn a dictionary in situ on the sampled image, for joint reconstruction and demosaicking. We use global-local shrinkage priors to learn the dictionary and dictionary coefficients representing the data. Simulation results demonstrate the feasibility of the proposed camera and the superior performance of the Bayesian shrinkage dictionary learning algorithm.