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

• URL: http://arxiv.org/abs/2301.06084v2
• Date: Fri, 6 Sep 2024 09:22:17 GMT
• Title: Scattering-induced entropy boost for highly-compressed optical sensing and encryption
• Authors: Xinrui Zhan, Xuyang Chang, Daoyu Li, Rong Yan, Yinuo Zhang, Liheng Bian,
• Abstract summary: Image sensing often relies on a high-quality machine vision system with a large field of view and high resolution. We propose a novel image-free sensing framework for resource-efficient image classification. The proposed framework is shown to obtain over a 95% accuracy at sampling rates of 1% and 5% for classification on the MNIST dataset.
• Score: 7.502671257653539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image sensing often relies on a high-quality machine vision system with a large field of view and high resolution. It requires fine imaging optics, has high computational costs, and requires a large communication bandwidth between image sensors and computing units. In this paper, we propose 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 for single-pixel detection, the optical field for 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 field of view and improving the system's security. The one-dimensional sequence of intensity values, which is 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 obtain over a 95\% accuracy at sampling rates of 1% and 5% for classification on the MNIST dataset and the recognition of Chinese license plates, respectively, and the framework is up to 24% more efficient than the approach without an optical diffuser. The proposed framework represents a significant breakthrough in high-throughput machine intelligence for scene analysis with low bandwidth, low costs, and strong encryption.

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