Can the success of digital super‐resolution networks be transferred to passive all‐optical systems?

The deep learning revolution has increased the demand for computational resources, driving interest in efficient alternatives like all-optical diffractive neural networks (AODNNs). These systems operate at the speed of light without consuming external energy, making them an attractive platform for energy-efficient computation. One task that could greatly benefit from an all-optical implementation is spatial super-resolution. This would allow overcoming the fundamental resolution limitation of conventional optical systems, dictated by their numerical aperture. Here, we examine whether the success of digital super-resolution networks can be replicated with AODNNs considering networks with phase-only nonlinearities. We find that while promising, super-resolution AODNNs face two key physical challenges: (i) a tradeoff between reconstruction fidelity and energy preservation along the optical path and (ii) a limited dynamic range of input intensities that can be effectively processed. These findings offer a first step toward understanding and addressing the design constraints of passive, all-optical super-resolution systems.