Learned phase coded aperture for the benefit of depth of field extension

Shay Elmalem*, Raja Giryes, Emanuel Marom

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

Modern consumer electronics market dictates the need for small-scale and high-performance cameras. Such designs involve trade-o s between various system parameters. In such trade-o s, Depth Of Field (DOF) is a significant issue very often. We propose a computational imaging-based technique to overcome DOF limitations. Our approach is based on the synergy between a simple phase aperture coding element and a convolutional neural network (CNN). The phase element, designed for DOF extension using color diversity in the imaging system response, causes chromatic variations by creating a di erent defocus blur for each color channel of the image. The phase-mask is designed such that the CNN model is able to restore from the coded image an all-in-focus image easily. This is achieved by using a joint end-to-end training of both the phase element and the CNN parameters using backpropagation. The proposed approach provides superior performance to other methods in simulations as well as in real-world scenes.

Original languageEnglish
Pages (from-to)15316-15331
Number of pages16
JournalOptics Express
Volume26
Issue number12
DOIs
StatePublished - 11 Jun 2018

Funding

FundersFunder number
Horizon 2020 Framework Programme757497
H2020 European Research Council
European Research Council

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