Image-free single-pixel sensing

The conventional high-level sensing techniques require high-fidelity images to extract visual features, which consume high software complexity or high hardware complexity. We present the single-pixel sensing (SPS) technique that performs high-level sensing directly from a small amount of coupled single-pixel measurements, without the conventional image acquisition and reconstruction process. The technique consists of three steps, including binarized light modulation, single-pixel coupled detection, and end-to-end deep-learning based decoding. The binarized modulation patterns are optimized with the decoding network by a two-step training strategy, leading to the least required measurements and optimal sensing accuracy. The effectiveness of SPS is experimentally demonstrated on the classification task of handwritten MNIST dataset, and 96% classification accuracy at ∼1kHz is achieved. The reported SPS technique is a novel framework for efficient machine intelligence, with low hardware and software complexity. Further, it maintains strong encryption.