Learning Optimal Phase-Coded Aperture for Depth of Field Extension

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
44 Downloads (Pure)

Abstract

We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages4315-4319
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - Sept 2019
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Keywords

  • Lenses
  • Apertures
  • Cameras
  • Convolution
  • Optimization
  • Optics
  • Computational imaging
  • Image deblurring
  • Neural network

Publication forum classification

  • Publication forum level 1

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