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Learning Optimal Phase-Coded Aperture for Depth of Field Extension

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


Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
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


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


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.


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

Publication forum classification

Field of science, Statistics Finland