Most conventional imaging modalities detect light indirectly by observing high-energy photons. The random nature of photon emission and detection is often the dominant sources of noise in imaging. Such case is referred to as photon-limited imaging, and the noise distribution is well modeled as Poisson. Multiplicative multiscale innovation (MMI) presents a natural model for Poisson count measurement, where the inter-scale relation is represented as random partitioning (binomial distribution) or local image contrast. In this paper, we propose a nonparametric empirical Bayes estimator that minimizes the mean square error of MMI coefficients. The proposed method achieves better performance compared with state-of-the-art methods in both synthetic and real sensor image experiments under low illumination.
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