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Multiscale Feature Tensor Train Rank Minimization for Multidimensional Image Recovery.

Hao Zhang, Xi-Le Zhao, Tai-Xiang Jiang

    IEEE Transactions on Cybernetics
    |September 20, 2021
    PubMed
    Summary

    This study introduces multiscale feature (MSF) tensorization for improved multidimensional image recovery, outperforming pixel-level methods. The novel approach enhances subsequent image recognition tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Tensor Analysis

    Background:

    • Traditional tensor-based methods for multidimensional image recovery rely on pixel-level low-rankness, which is unreliable with significant missing data.
    • This unreliability leads to detail loss, negatively impacting downstream applications like image recognition and segmentation.

    Purpose of the Study:

    • To propose a novel multiscale feature (MSF) tensorization method for robust multidimensional image recovery.
    • To enhance the performance of subsequent image analysis tasks by recovering features at a higher level.

    Main Methods:

    • Developed a novel multiscale feature (MSF) tensorization technique.
    • Proposed convex and nonconvex MSF tensor train rank minimization (MSF-TT) to jointly recover MSF and original tensors.

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  • Utilized alternating directional method of multipliers (ADMMs) for convex MSF-TT and proximal alternating minimization (PAM) for nonconvex MSF-TT.

    • Main Results:

    • The proposed MSF-TT method demonstrates superior performance in image recovery compared to existing approaches.
    • The recovered MSF tensor positively impacts subsequent image recognition tasks.
    • Theoretical convergence guarantees were established for the PAM algorithm.

    Conclusions:

    • MSF tensorization offers a more reliable approach to multidimensional image recovery, especially with extensive missing data.
    • The proposed MSF-TT framework effectively recovers both features and original image data.
    • This method significantly benefits image recognition applications by providing higher-quality feature representations.