Stitching sub-aperture in digital holography based on machine learning

Sub-aperture stitching in digital holography (DH) is a very important issue both for the spatial resolution improvement as well as for measuring larger aperture through synthetic enlargement of numerical aperture. In fact, sub-apertures stitching permits to greatly expand the capabilities of optical metrology thus allowing to accurately measure complex optical surfaces such as large spherical and aspheric. Stitching operations can be difficult and cumbersome depending on geometric parameters of specific objects under test. However, here we show that machine learning can definitively aid this process. In fact, here we propose for the first time, to the best of our knowledge, a novel sub-aperture stitching approach based on machine learning applied to an array of different phase-maps sub-apertures recorded by an off-axis digital holographic systems. Essentially, we construct a network according to computation model of sub-aperture stitching and remove the alignment errors and system aberration of sub-aperture maps by training the network. Correct measurement of the surface topography of hemisphere surface is demonstrated thus validating the proposed learning approach. Reported results demonstrate that machine learning can be a useful tool for simplifying the process and for making it a reliable and accurate tool in optical metrology.