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Matrix optimization for poisson compressed sensing

机译:泊松压缩感测的矩阵优化

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For compressed sensing of Poissonian measurements, there is a need for nonnegative measurement matrices. We seek an optimal measurement matrix that conserves energy. Moreover, the signals pass a known but uncontrolled mixing matrix, before being multiplexed and measured. This situation is relevant to various optical applications. We optimize the measurement matrix by mutual coherence minimization, under nonnegativity and energy conservation constraints. Nonnegativity excludes the known approach of seeking an equiangular tight frame as the optimal matrix. We thus seek a quasi-equiangular frame, which is approximated by a tight frame. Simulation results demonstrate superior reconstruction using our optimized matrices, compared to random nonnegative matrices.
机译:为了对泊松测量进行压缩感测,需要非负测量矩阵。我们寻求一种节省能源的最佳测量矩阵。此外,信号在被多路复用和测量之前,先通过已知但不受控制的混合矩阵。这种情况与各种光学应用有关。在非负性和节能约束下,我们通过相互相干最小化来优化测量矩阵。非负性排除了寻找等角紧密框架作为最佳矩阵的已知方法。因此,我们寻求准矩形框架,其近似为紧框架。仿真结果表明,与随机非负矩阵相比,使用我们的优化矩阵可实现出色的重构。

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