Abstract

Single-pixel imaging is a novel, to the best of our knowledge, computational imaging scheme, but a large number of measurements are typically required in data acquisition. Full-color single-pixel imaging takes many more measurements than does monochromatic single-pixel imaging. Utilizing the fact that human eyes have a poorer spatial resolution to blues than reds and greens, we propose to sample the blue component of color images with an ultra-low sampling ratio so as to reduce the number of measurements. We demonstrate our method with simulations and experiments, concluding that 95% of the measurements can be reduced in the acquisition of the blue component of natural color images in the size of ${256} \times {256}$ pixels, and the resulting images are without remarkable visual loss. Moreover, utilizing the sparsity of natural images, the sampling ratios of the red and green components can be reduced to 15% and 50%, respectively. This Letter may generate a new insight of how to optimize the imaging efficiency by utilizing human vision properties. The proposed method can be adopted by other full-color computational imaging techniques.

© 2020 Optical Society of America

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