Light Field Compressed Sensing over a Disparity-Aware Dictionary

Jie Chen, Lap Pui Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)

Abstract

Light field (LF) acquisition faces the challenge of extremely bulky data. Available hardware solutions usually compromise the sensor resource between spatial and angular resolutions. In this paper, a compressed sensing framework is proposed for the sampling and reconstruction of a high-resolution LF based on a coded aperture camera. First, an LF dictionary based on perspective shifting is proposed for the sparse representation of the highly correlated LF. Then, two separate methods, i.e., subaperture scan and normalized fluctuation, are proposed to acquire/calculate the scene disparity, which will be used during the LF reconstruction with the proposed disparity-Aware dictionary. At last, a hardware implementation of the proposed LF acquisition/reconstruction scheme is carried out. Both quantitative and qualitative evaluation show that the proposed methods produce the state-of-The-Art performance in both reconstruction quality and computation efficiency.

Original languageEnglish
Article number7368916
Pages (from-to)855-865
Number of pages11
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume27
Issue number4
DOIs
Publication statusPublished - Apr 2017
Externally publishedYes

Keywords

  • Compressed sensing
  • light field (LF)
  • perspective shifting
  • sparse representation

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

Cite this