Automatic depth map retrieval from digital holograms using a deep learning approach

Abstract

Information extraction from computer-generated holograms using learning-based methods is a topic that has not received much research attention. In this article, we propose and study two learning-based methods to extract the depth information from a hologram and compare their performance with that of classical depth from focus (DFF) methods. We discuss the main characteristics of a hologram and how these characteristics can affect model training. The obtained results show that it is possible to extract depth information from a hologram if the problem formulation is well-posed. The proposed methods are faster and more accurate than state-of-the-art DFF methods.