4D wavefront sensing using dynamic neural image sharpening

In this paper, we develop a dynamic framework for wavefront sensing and image sharpening from a video sequence of data collected by a digital-holographic sensor. We build on recent progress using implicit neural representations (INRs) to model the complex phase errors caused by atmospheric turbulence. INRs leverage unsupervised learning to form a continuous functional representation of the atmospheric phase errors. In turn, we extend our framework from modeling 3D phase errors from a single digital-holographic measurement (consisting of three spatial coordinates, x , y , and z ), to modeling 4D phase errors from a time sequence of digital-holographic measurements (consisting of three spatial coordinates plus time, x , y , z , and t ). By introducing additional time-varying measurements, we better model the dynamic behavior of atmospheric turbulence. This improved modeling provides three benefits: (1) it helps constrain our under-determined estimation problem, which improves phase-error estimation quality and image reconstruction; (2) it allows us to estimate phase errors between actual measurements, increasing the effective sampling rate of our sensor; and (3) it allows us to predict future phase errors, helping us reduce latency in our estimation pipeline. At large, (1)–(3) creates numerous benefits for applications where 4D phase errors naturally arise (e.g., microscopy, metrology, lidar, remote sensing, etc.).