Dual-beam differential optical imaging for atmospheric turbulence mitigation in free-space propagation

Atmospheric turbulence imposes a fundamental barrier to reliable object detection in consumer-grade free-space optical (FSO) systems, degrading performance in applications such as drone navigation, smart surveillance, and portable satellite terminals. Current approaches, exemplified by the hybrid adversarial contrastive framework (HACF), operate in the post-acquisition domain, treating turbulence as irrecoverable stochastic noise and relying on statistical pseudo-label refinement to mitigate its effects. We introduce differential beam imaging (DBI), a physics-driven, acquisition-layer paradigm that transcends this limitation by physically isolating object reflectance at the moment of signal capture. DBI exploits the spatial correlation of atmospheric turbulence through simultaneous dual-beam probing; one beam illuminates the object, while an adjacent reference beam captures only the turbulence field. By differencing these measurements, DBI cancels turbulence at its source, preserving the object’s structural integrity before any information loss occurs. We formalize and solve four non-incremental scientific problems: (P1) establishing the wave-optics-based spatial correlation regime for valid cancellation, (P2) optimizing beam separation under turbulence crosstalk trade-offs, (P3) designing a Siamese-cross attention network (SCAN) that learns turbulence-invariant features from differential image pairs, and (P4) co-designing a real-time, low-power (