Deep Sparse Light Field Refocusing

Shachar Ben Dayan, David Mendlovic, Raja Giryes

Research output: Contribution to conferencePaperpeer-review

Abstract

Light field photography enables to record 4D images, containing angular information alongside spatial information of the scene. One of the important applications of light field imaging is post-capture refocusing. Current methods require for this purpose a dense field of angle views; those can be acquired with a micro-lens system or with a compressive system. Both techniques have major drawbacks to consider, including bulky structures and angular-spatial resolution trade-off. We present a novel implementation of digital refocusing based on sparse angular information using neural networks. This allows recording high spatial resolution in favor of the angular resolution, thus, enabling to design compact and simple devices with improved hardware as well as better performance of compressive systems. We use a novel convolutional neural network whose relatively small structure enables fast reconstruction with low memory consumption. Moreover, it allows handling without re-training various refocusing ranges and noise levels. Results show major improvement compared to existing methods.

Original languageEnglish
StatePublished - 2020
Event31st British Machine Vision Conference, BMVC 2020 - Virtual, Online
Duration: 7 Sep 202010 Sep 2020

Conference

Conference31st British Machine Vision Conference, BMVC 2020
CityVirtual, Online
Period7/09/2010/09/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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