Harnessing speckle images: efficient extraction of hidden information

Scattering obscures information carried by waves by producing speckle patterns, posing a fundamental challenge across diverse fields, from microscopy to astronomy. Although machine learning has recently shown promise in speckle analysis, existing approaches are hindered by their dependence on large, labeled datasets—a significant bottleneck in many real-world applications. Here, we introduce speckle unsupervised recognition and evaluation (SURE), a groundbreaking unsupervised learning strategy for speckle recognition that eliminates the need for labeled training data. SURE’s distinctive feature lies in its ability to extract invariant features through advanced clustering algorithms to enable direct classification of high-level information from speckle patterns without prior knowledge. We demonstrate the transformative potential of this approach in two key applications: (1) a noninvasive glucose monitoring system that accurately tracks glucose concentrations over time without extensive calibration and (2) a high-throughput communication system using multimode fibers, achieving improved performance in dynamic environments. In addition, we showcase SURE’s unprecedented capability to classify objects hidden behind obstacles using scattered light, further broadening its scope. This versatile approach opens new frontiers in biomedical diagnostics, quantum network decoupling, and remote sensing, unlocking a transformative new paradigm for extracting information from seemingly random optical patterns.