Abstract:Imaging techniques involve counting of photons striking a detector. Due to fluctuations in the counting process, the measured photon counts are known to be corrupted by Poisson noise. In this paper, we propose a blind dictionary learning framework for the reconstruction of photographic image data from Poisson corrupted measurements acquired by a compressive camera. We exploit the inherent non-negativity of the data by modeling the dictionary as well as the sparse dictionary coefficients as non-negative entitie… Show more
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