Data-centric approach for miscellaneous optical sensing and imaging

In this paper, several methods based on a data-centric approach for optical sensing and imaging are summarized and their potential capabilities for miscellaneous problems are presented. At the beginning, the framework of data-centric approach is explained briefly with a generalized formulation of a process of optical sensing and imaging. The essential idea is application of machine learning to estimate the inverse process of the target optical sensing and imaging using mathematical models. Once such an estimation is achieved, the input object and the resultant output signals can be related by the mathematical model. Based on the framework, several problems in optical sensing and imaging are demonstrated. They are single-shot superresolution in diffractive imaging, computer-generated holography based on deep learning, and wavefront sensing using deep learning. These examples are not just simple imaging but sophisticated methods in general optical sensing and imaging. The data-centric approach is expected to be useful in various problems in applied optics.