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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing.

Zhonghao Zhang, Yipeng Liu, Jiani Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We introduce AMP-Net, a deep unfolding model for compressive sensing (CS) image reconstruction. It achieves high accuracy and speed by unfolding the approximate message passing algorithm and integrating deblocking modules.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Machine Learning

    Background:

    • Compressive sensing (CS) reconstruction methods are typically model-based or deep network-based.
    • Deep unfolding methods combine interpretability of model-based approaches with the speed of deep networks.

    Purpose of the Study:

    • To propose AMP-Net, a novel deep unfolding model for visual image compressive sensing reconstruction.
    • To address blocking artifacts common in CS image reconstruction.

    Main Methods:

    • Developed AMP-Net by unfolding the iterative denoising process of the approximate message passing (AMP) algorithm.
    • Integrated deblocking modules within the network architecture.
    • Jointly trained the sampling matrix with other network parameters.

    Main Results:

    • AMP-Net demonstrated superior reconstruction accuracy compared to existing state-of-the-art methods.
    • Achieved high reconstruction speed.
    • Required a small number of network parameters for effective performance.

    Conclusions:

    • AMP-Net offers an effective solution for visual image CS reconstruction.
    • The method balances reconstruction accuracy, speed, and model complexity.