Compressive light field imaging with weighted random projections

Amit Ashok, Mark A. Neifeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traditional light field imagers do not exploit the inherent spatio-angular correlations in light field of natural scenes towards reducing the number of measurements and minimizing the spatio-angular resolution trade-off. Here we describe a compressive light field imager that utilizes the prior knowledge of sparsity/compressibility along the spatial dimension of the light field to make compressive measurements. The reconstruction performance is analyzed for three choices of measurement bases: wavelet, random, and weighted random using a simulation study. We find that the weighted random bases outperforms both the coherent wavelet basis and the incoherent random basis on a light field data set. Specifically, the simulation study shows that the weighted random basis achieves 44% to 50% lower reconstruction error compared to wavelet and random bases for a compression ratio of three.

Original languageEnglish (US)
Title of host publicationUnconventional Imaging, Wavefront Sensing, and Adaptive Coded Aperture Imaging and Non-Imaging Sensor Systems
DOIs
StatePublished - 2011
EventUnconventional Imaging, Wavefront Sensing, and Adaptive Coded Aperture Imaging and Non-Imaging Sensor Systems - San Diego, CA, United States
Duration: Aug 21 2011Aug 25 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8165
ISSN (Print)0277-786X

Other

OtherUnconventional Imaging, Wavefront Sensing, and Adaptive Coded Aperture Imaging and Non-Imaging Sensor Systems
Country/TerritoryUnited States
CitySan Diego, CA
Period8/21/118/25/11

Keywords

  • Compressive imaging
  • Discrete Wavelet transform
  • Light Field
  • Random projections
  • Structured sparsity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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