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    Imaging in scattering media using correlation image sensors and sparse convolutional coding

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    Name:
    PMD-ScatteringMedia.pdf
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    23.71Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Heide, Felix
    Xiao, Lei
    Kolb, Andreas
    Hullin, Matthias B.
    Heidrich, Wolfgang cc
    KAUST Department
    Computational Imaging Group
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2014-10-17
    Online Publication Date
    2014-10-17
    Print Publication Date
    2014-10-20
    Permanent link to this record
    http://hdl.handle.net/10754/556516
    
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    Abstract
    Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.
    Citation
    Imaging in scattering media using correlation image sensors and sparse convolutional coding 2014, 22 (21):26338 Optics Express
    Publisher
    The Optical Society
    Journal
    Optics Express
    DOI
    10.1364/OE.22.026338
    Additional Links
    http://www.opticsinfobase.org/abstract.cfm?URI=oe-22-21-26338
    ae974a485f413a2113503eed53cd6c53
    10.1364/OE.22.026338
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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