Meta-Learning Approach in Diffractive Lens Computational Imaging

Currently, more and more research projects are being carried out in the field of creating flat optics-based imaging systems. The use of flat optics allows one to significantly reduce the weight and simplify the design of the optical system, which is a great advantage over classic refractive analogs. The main drawback of flat optical elements is significant reduction in the quality of captured images due to strong chromatic aberration. This article shows how this degradation in quality can be dealt with using a Model-Agnostic Meta-Learning approach.