Optical smart imaging through turbulent media

In real life, it is usually required to provide an imaging through dynamic scattering environments, such as atmospheric turbulence, fog, and biological tissues. As known, such environments strongly limit the imaging resolution, and quite often the distorted images are hard to identify visually. Adaptive optics (AO) is one of the state-of-the-art techniques to deal with that. In traditional AO, the distorted wavefront of the object is measured and a deformable mirror is used to compensate for wavefront perturbations. However, AO is getting problematic for complex objects and strong turbulence due to the lag time in the feedback process. Recently, we have proposed an optical computing approach that can predict large chaotic systems with a performance achievable by large supercomputers only. This proposal aims using the optical computing in AO to overcome the challenge of imaging through the strong turbulences, as new changes necessary on the mirror can be predicted ahead of time. The obtained results will have a strong impact in both fields of optical imaging and optical computing. If successful, our new technique will be one of the most advanced real-world applications of optical computing and can be implemented on various types of flying objects in the future.