Non-imaging single-pixel sensing with optimized binary modulation

TL;DR

This paper introduces a novel single-pixel sensing framework that performs high-level classification directly from coupled measurements using optimized binary modulation and deep learning, eliminating the need for image reconstruction.

Contribution

It proposes an end-to-end deep learning approach with optimized binary modulation for high-level sensing directly from single-pixel measurements, reducing hardware and software complexity.

Findings

Achieved 96.68% classification accuracy on MNIST dataset.

Operates at approximately 1kHz measurement rate.

Demonstrated effective high-level sensing without image reconstruction.

Abstract

The conventional high-level sensing techniques 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 high-level sensing directly from coupled measurements of a single-pixel detector, without the conventional image acquisition and reconstruction process. The technique consists of three steps including binary light modulation that can be physically implemented at $\sim$22kHz, single-pixel coupled detection owning wide working spectrum and high signal-to-noise ratio, and end-to-end deep-learning based sensing that reduces both hardware and software complexity. Besides, the binary modulation is trained and optimized together with the sensing network, which ensures least required measurements and optimal sensing accuracy. The effectiveness of SPS is demonstrated on the classification task of handwritten MNIST dataset, and 96.68% classification accuracy at $\sim$1kHz is achieved. The reported single-pixel sensing technique is a novel framework for highly efficient machine intelligence.