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    Consensus Convolutional Sparse Coding

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    Choudhury_Consensus_Convolutional_Sparse_ICCV_2017_paper.pdf
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    Type
    Conference Paper
    Authors
    Choudhury, Biswarup
    Swanson, Robin J. cc
    Heide, Felix
    Wetzstein, Gordon
    Heidrich, Wolfgang cc
    KAUST Department
    Visual Computing Center, King Abdullah University of Science and Technology (KAUST)
    Date
    2017-12-25
    Online Publication Date
    2017-12-25
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/626836
    
    Metadata
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    Abstract
    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
    Citation
    Choudhury, B., Swanson, R., Heide, F., Wetzstein, G., & Heidrich, W. (2017). Consensus Convolutional Sparse Coding. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.459
    Sponsors
    VCC KAUST Baseline Funding, Terman Faculty Fellowship, Intel Compressive Sensing Alliance, National Science Foundation (IIS 1553333), and NSF/Intel Partnership on Visual and Experiential Computing (IIS 1539120).
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Xplore
    Conference/Event name
    International Conference on Computer Vision (ICCV)
    DOI
    10.1109/ICCV.2017.459
    Additional Links
    http://ieeexplore.ieee.org/document/8237721/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2017.459
    Scopus Count
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