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

TL;DR

This paper introduces a novel optical sensing framework that uses scattering and modulation to drastically reduce measurements needed for image classification, enhancing security and efficiency in resource-constrained environments.

Contribution

It presents a new image-free sensing method combining scattering, modulation, and deep learning for highly efficient, secure, and low-bandwidth image classification.

Findings

Achieves over 95% accuracy at 1% and 5% sampling rates on MNIST and license plates.

Reduces measurement requirements by up to 100 times compared to traditional methods.

Improves efficiency by up to 24% with an optical diffuser.

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.