Scattering-induced entropy boost for highly-compressed optical sensing and encryption

Image classification often relies on a high-quality machine vision system with a large view field and high resolution, demanding fine imaging optics, heavy computational costs, and large communication bandwidths between an image sensor and a computing unit. Here, we report a novel image-free sensing framework for resource efficient image classification where the required number of measurements can be reduced by up to two orders of magnitude. In the proposed framework of single-pixel detection, the optical field from a target is first scattered by an optical diffuser and then two-dimensionally modulated by a spatial light modulator. The optical diffuser simultaneously serves as a compressor and an encryptor for the target information, effectively narrowing the view field and improving the system’s security. The one-dimensional sequence of intensity values, measured with time-varying patterns on the spatial light modulator, is then used to extract semantic information based on end-to-end deep learning. The proposed sensing framework is shown to provide over 95% accuracy with the sampling rate of 1% and 5%, respectively, for the classification of MNIST dataset and the recognition of Chinese license plate, which was up to 24 % more efficient compared with the case without an optical diffuser. The proposed framework represents a significant breakthrough in realizing high-throughput machine intelligence for scene analysis, with low -bandwidth, low cost, and strong encryption.