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    ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

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    ISTANET-CVPR2018.pdf
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    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Zhang, Jian
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center (VCC)
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-12-18
    Online Publication Date
    2018-12-18
    Print Publication Date
    2018-06
    Permanent link to this record
    http://hdl.handle.net/10754/656532
    
    Metadata
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    Abstract
    With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (e.g. nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed ISTA-Net+, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed. Our source codes are available: http://jianzhang.tech/projects/ISTA-Net.
    Citation
    Zhang, J., & Ghanem, B. (2018). ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi:10.1109/cvpr.2018.00196
    Publisher
    IEEE Computer Society
    Conference/Event name
    31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
    DOI
    10.1109/CVPR.2018.00196
    arXiv
    1706.07929
    Additional Links
    https://ieeexplore.ieee.org/document/8578294/
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2018.00196
    Scopus Count
    Collections
    Conference Papers; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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