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Related Experiment Video

Updated: Sep 22, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery.

Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    Summary
    This summary is machine-generated.

    This study introduces a novel nonlinear neural network for tensor recovery in multi-dimensional imaging. The method learns a nonlinear transform for improved tensor completion and other imaging tasks.

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    Area of Science:

    • Multi-dimensional imaging
    • Tensor decomposition
    • Machine learning

    Background:

    • Transform-based tensor nuclear norm (TNN) minimization is gaining traction for third-order tensor recovery in multi-dimensional imaging.
    • Existing methods often rely on linear transforms, limiting their ability to capture complex tensor structures.

    Purpose of the Study:

    • To propose a novel nonlinear multilayer neural network for learning nonlinear transforms in a self-supervised manner.
    • To enhance tensor recovery by leveraging low-rank representations and data-fitting.

    Main Methods:

    • A nonlinear multilayer neural network is developed to learn a nonlinear transform directly from observed tensor data.
    • The network utilizes the low-rank property of the transformed tensor and enforces data-fitting between observed and reconstructed tensors.
    • The approach operates in a self-supervised fashion, requiring only the observed tensor as input.

    Main Results:

    • The proposed nonlinear transform learning method demonstrates superior performance across various tasks.
    • Evaluated on tensor completion, background subtraction, robust tensor completion, and snapshot compressive imaging.
    • Outperforms existing state-of-the-art methods on diverse datasets and applications.

    Conclusions:

    • The developed nonlinear neural network effectively learns data-driven nonlinear transforms for tensor recovery.
    • This self-supervised approach offers significant improvements over traditional linear transform methods.
    • The method shows broad applicability and superior performance in complex multi-dimensional imaging problems.