English

Sparse deep computer-generated holography for optical microscopy

Optics 2021-12-14 v3 Machine Learning

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 zz-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.

Keywords

Cite

@article{arxiv.2111.15178,
  title  = {Sparse deep computer-generated holography for optical microscopy},
  author = {Alex Liu and Yi Xue and Laura Waller},
  journal= {arXiv preprint arXiv:2111.15178},
  year   = {2021}
}

Comments

5 pages, 4 figures, to be presented at NeurIPS 2021 Deep Learning and Inverse Problems workshop

R2 v1 2026-06-24T07:57:13.086Z