Computational aspects and perspectives of novel modalities for visible and nonvisible imaging

Authors

  • Ritz Ann P. Aguilar ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

Computational imaging overcomes the limitations of conventional imaging in both visible and nonvisible regimes. It utilizes highly-efficient computer algorithms — from compressed sensing to machine learning — integrated with imaging hardware to produce high-quality images using relatively low-cost setups. Single-pixel imaging, for example, uses only one detector to image a target scene by correlating the detector signal with modulated light patterns. Computational microscopy setups, specifically ptychography-based ones, are now capable of imaging biological samples that were previously optically irresolvable without the need for high numerical aperture lenses. This talk explores the mechanisms of these unconventional imaging systems that are highly adaptable and performs accurate imaging at any wavelength.

About the Speaker

Ritz Ann P. Aguilar, National Institute of Physics, University of the Philippines Diliman

Ritz Ann Aguilar is currently an Assistant Professor of Physics at the University of the Philippines Diliman. She earned her doctorate degree in Physics in 2020 from the same university. Her topics of interest include optics, image processing, computational imaging, and instrumentation. She is set to join the Institute for Radiation Physics at the Helmholtz Research Center in Dresden, Germany as a postdoctoral researcher in 2023 where she will work on synchrotron radiation experiments, inverse imaging problems, and optimization for photon science applied to clinical research.

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Issue

Article ID

SPP-2022-INV-1C-01

Section

Invited Presentations

Published

2022-10-02

How to Cite

[1]
RAP Aguilar, Computational aspects and perspectives of novel modalities for visible and nonvisible imaging, Proceedings of the Samahang Pisika ng Pilipinas 40, SPP-2022-INV-1C-01 (2022). URL: https://proceedings.spp-online.org/article/view/SPP-2022-INV-1C-01.