Deep Learning Models for Digital Holography

Description

Digital Holography is an emerging field of a new paradigm for general, as well as microscopic imaging applications. The goal of the project is to conduct experiments for the processing of digital holograms using deep neural networks as an approach to AI-based/semantic large scale and complex information and content analysis. With the usage of deep neural networks, this project develops new techniques for a more robust and performant processing of digital holograms, especially for the task of numerical hologram reconstruction. We aim to explore the semantic segmentation network models, and challenge to improve the performance of processing holograms. Among the most popular network models, the architecture called U-Net was chosen to achieve this task. In these works, several experiments were conducted applying this method to artificially generated holograms, with promising results. These positive results lead to the believe that a significant improvement could be made on real holographic data, that could lead to achieving a more robust hologram reconstruction.