Non-imaging single-pixel sensing with optimized binary modulation
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The conventional high-level sensing tasks such as image classification require high-fidelity images as input to extract target features, which are produced by either complex imaging hardware or high-complexity reconstruction algorithms. In this letter, we propose single-pixel sensing (SPS) that performs sensing tasks directly from coupled measurements of a single-pixel detector, without the conventional image acquisition and reconstruction process. We build a deep convolutional neural network that comprises a target encoder and a sensing decoder. The encoder simulates the single-pixel detection and employs binary modulation that can be physically implemented at 22kHz. Both the encoder and decoder are trained together for optimal sensing precision. The effectiveness of SPS is demonstrated on the classification task of handwritten MNIST dataset, and achieves 96.68 to the conventional imaging-sensing framework, the reported SPS technique requires less measurements for fast sensing rate, maintains low computational complexity, wide working spectrum and high signal-to-noise ratio, and is further beneficial for communication and encryption.
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