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Acquiring Dynamic Light Fields Through Coded Aperture Camera

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12364)

Abstract

We investigate the problem of compressive acquisition of a dynamic light field. A promising solution for compressive light field acquisition is to use a coded aperture camera, with which an entire light field can be computationally reconstructed from several images captured through differently-coded aperture patterns. With this method, it was assumed that the scene should not move throughout the complete acquisition process, which restricted real applications. In this study, however, we assume that the target scene may change over time, and propose a method for acquiring a dynamic light field (a moving scene) using a coded aperture camera and a convolutional neural network (CNN). To successfully handle scene motions, we develop a new configuration of image observation, called V-shape observation, and train the CNN using a dynamic-light-field dataset with pseudo motions. Our method is validated through experiments using both a computer-generated scene and a real camera.

Keywords

Light field CNN Coded aperture camera 

Supplementary material

504475_1_En_22_MOESM1_ESM.pdf (21 kb)
Supplementary material 1 (pdf 21 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of EngineeringNagoya UniversityNagoyaJapan
  2. 2.Institute for Datability ScienceOsaka UniversitySuitaJapan

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