Provable and Robust Wavefront Sensing via Self-Reference Interferometry

A team from Carnegie Mellon University led by Nebiyou Yismaw, Vishwanath Saragadam, Aswin C. Sankaranarayanan, and M. Salman Asif has developed a breakthrough wavefront sensing technique called Self-Reference Interferometry. The method solves the fundamental problem of phase retrieval—recovering both phase and intensity information from light—which conventional image sensors cannot measure directly. Unlike traditional phase-shifting interferometry (PSI) that requires stable reference beams difficult to maintain in real-world settings, their approach creates interference between shifted copies of the incoming wave itself.

The framework generates pairwise phase differences between shifted pixels and formulates an analytical solution that propagates these differences across a mathematically connected graph. The researchers proved that using co-prime shifts guarantees graph connectivity and bounds worst-case error accumulation, making the method both provably robust and theoretically sound. Extensive simulations demonstrate the system can recover complete phase profiles from just eight shifted measurements, outperforming existing approaches in both efficiency and practicality.

Finally, the team validated their theoretical framework with a hardware prototype, demonstrating real-world applications including optical phase profile recovery, auto-refocusing, and imaging through scattering media. This represents a significant advancement toward making high-precision wavefront sensing accessible outside controlled laboratory environments, potentially transforming fields from astronomy to medical imaging.