Seeing the Invisible through Speckle Images

TL;DR

This paper introduces an unsupervised machine learning approach to interpret speckle patterns, enabling high-level information extraction without labeled data, demonstrated through biomedical and communication applications.

Contribution

The paper presents a novel unsupervised learning strategy for speckle recognition that captures invariant features for classification without requiring labeled datasets.

Findings

Successfully differentiated glucose concentrations noninvasively.

Enabled high-throughput communication through multimode fibers.

Validated versatility across biomedical and optical communication applications.

Abstract

Scattering obscures information carried by wave by producing a speckle pattern, posing a common challenge across various fields, including microscopy and astronomy. Traditional methods for extracting information from speckles often rely on significant physical assumptions, complex devices, or intricate algorithms. Recently, machine learning has emerged as a scalable and widely adopted tool for interpreting speckle patterns. However, most current machine learning techniques depend heavily on supervised training with extensive labeled datasets, which is problematic when labels are unavailable. To address this, we propose a strategy based on unsupervised learning for speckle recognition and evaluation, enabling to capture high-level information, such as object classes, directly from speckles without labeled data. By deriving invariant features from speckles, this method allows for the classification of speckles and facilitates diverse applications in image sensing. We experimentally validated our strategy through two significant applications: a noninvasive glucose monitoring system capable of differentiating time-lapse glucose concentrations, and a high-throughput communication system utilizing multimode fibers in dynamic environments. The versatility of this method holds promise for a broad range of far-reaching applications, including biomedical diagnostics, quantum network decoupling, and remote sensing.