Light Field Compressed Sensing Over a Disparity-Aware Dictionary

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...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2017-04, Vol.27 (4), p.855-865
Main Authors: Chen, Jie, Chau, Lap-Pui
Format: Article
Language:English
Subjects:
Apertures
Cameras
Compressed sensing
Computing time
Dictionaries
Hardware
Image reconstruction
light field (LF)
perspective shifting
Production methods
Reconstruction
sparse representation
Spatial resolution
State of the art
Variations
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Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2015.2513485