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This paper explores the use of Poisson sparse decomposition methods for computationally separating tumor nuclei from normal tissue structures in photon-limited microendoscopic images. Sparse decomposition tools are a natural fit for this application with promising preliminary results. However, there are significant the tradeoffs among different algorithms used for Poisson sparse decomposition which are described in detail and demonstrated via simulation.

This paper describes an adaptive compressive coded aperture imaging system for video based on motion-compensated video sparsity models. In particular, motion models based on optical flow and sparse deviations from optical flow (i.e. salient motion) can be used to (a) predict future video frames from previous compressive measurements, (b) perform reconstruction using efficient online convex programming techniques, and (c) adapt the coded aperture to yield higher reconstruction fidelity in the vicinity of this salient motion.