Single-pixel imaging with sampling distributed over simplex vertices

Abstract

We propose a method of reduction of experimental noise in single-pixel imaging by expressing the subsets of sampling patterns as linear combinations of vertices of a multidimensional regular simplex. This method may be also directly extended to complementary sampling. The modified sampling consists only of non-negative patterns. The measurement becomes theoretically independent of the ambient illumination, and in practice becomes more robust to the varying conditions of the experiment. We show how the optimal dimensionality of the simplex depends on the level of measurement noise. We present experimental results of single-pixel imaging using binarized sampling and a real-time reconstruction with the Fourier domain regularized inversion method.

References in corpus (8)

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• Single-pixel 3D imaging with time-based depth resolution
• Hadamard single-pixel imaging versus Fourier single-pixel imaging
• Normalized Ghost Imaging
• Real-time single-pixel video imaging with Fourier domain regularization
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Cited by in corpus (4)

• Optical machine learning with incoherent light and a single-pixel detector
• Single pixel imaging at high pixel resolutions
• Differential real-time single-pixel imaging with Fourier domain regularization -- applications to VIS-IR imaging and polarization imaging
• Domino successive-deviation ghost imaging