  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 percentage accuracy with the sampling rate of 1 percentage and 5
percentage, respectively, for the classification of MNIST dataset and the
recognition of Chinese license plate, which was up to 24 percentage 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.
