Mapping 2D spatial structured light information onto 1D temporal speckle sequences

Conventional structured light recognition methods rely on spatially resolved imaging. These systems often suffer from low frame rates, sensitivity to alignment, and high computational demands. Such limitations hinder their use in real-time and scalable applications. Here, we demonstrate a novel approach, to our knowledge, for structured light recognition by mapping two-dimensional spatial features onto one-dimensional temporal speckle sequences. This is achieved using a single-pixel detector that captures temporal fluctuations in speckle patterns produced by a rotating diffuser. We demonstrate that optimal mapping occurs when the detector size is equal to or greater than the average speckle grain size, ensuring effective mapping of spatiotemporal speckle dynamics. Utilizing this principle, we successfully recognize Laguerre-Gaussian, Hermite-Gaussian, and intensity-degenerate perfect vortex beams via a support vector machine classifier. The recognition model exhibits >99<i>%</i> accuracy and is robust to atmospheric turbulence, strict optical alignments, or symmetry-breaking optics. Furthermore, we demonstrate a proof-of-concept of the proposed method by establishing a free-space optical communication channel. Employing 16 orbital angular momentum superposition states utilizing a 4-bit binary amplitude switching scheme, we achieve a bit error rate of 0.001. This work presents a scalable, low-latency, and computationally efficient method for real-time structured light recognition, offering significant potential for next-generation optical communication and sensing systems.