Sparse deep computer-generated holography for optical microscopy

TL;DR

This paper introduces a novel sparse deep CGH algorithm that uses an unsupervised generative model to produce high-contrast, sparsely distributed 3D illumination patterns for optical microscopy, enhancing existing holography techniques.

Contribution

The paper presents a new deep learning-based CGH algorithm tailored for 3D microscopy, enabling efficient, high-contrast sparse illumination generation.

Findings

Generates high-contrast 3D illumination patterns

Produces sparsely distributed points in large volumes

Outperforms conventional CGH algorithms in contrast

Abstract

Computer-generated holography (CGH) has broad applications such as direct-view display, virtual and augmented reality, as well as optical microscopy. CGH usually utilizes a spatial light modulator that displays a computer-generated phase mask, modulating the phase of coherent light in order to generate customized patterns. The algorithm that computes the phase mask is the core of CGH and is usually tailored to meet different applications. CGH for optical microscopy usually requires 3D accessibility (i.e., generating overlapping patterns along the $z$-axis) and micron-scale spatial precision. Here, we propose a CGH algorithm using an unsupervised generative model designed for optical microscopy to synthesize 3D selected illumination. The algorithm, named sparse deep CGH, is able to generate sparsely distributed points in a large 3D volume with higher contrast than conventional CGH algorithms.