A hybrid lens: Integrating neural lens and optical lens on the Fourier plane

Alireza Khodaei, Jitender Deogun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we introduce a concept of the hybrid lens as a novel form of optical information processing apparatus that integrates the conventional optical lenses and the recently proposed neural lens, that is, an image post-processing technique based on generative convolution neural networks (GCNN). This integration is based on leveraging the fact that the Fourier plane is the common working principle of both types of lens. We demonstrate how manipulating the coherent light's spectral components on the Fourier plane behind a biconvex lens is a computationally-free alternative to performing convolution matrix operation in GCNN, which involves high computational expenses. In our approach, the GCNN can perform image generation in a shorter time. Our hybrid lens only requires computational power at the levels that the embedded resources on medical devices can afford. This feature is very important for commercialization as it allows making standalone units like microscopes without relying on external resources such as computing clouds.

Original languageEnglish (US)
Title of host publicationAI and Optical Data Sciences II
EditorsBahram Jalali, Ken-ichi Kitayama
PublisherSPIE
ISBN (Electronic)9781510642416
DOIs
StatePublished - 2021
EventAI and Optical Data Sciences II 2021 - Virtual, Online, United States
Duration: Mar 6 2021Mar 11 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11703
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceAI and Optical Data Sciences II 2021
Country/TerritoryUnited States
CityVirtual, Online
Period3/6/213/11/21

Keywords

  • convolutional neural networks
  • Fourier plan
  • generative neural networks
  • Hybrid lens
  • optical information processing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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