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FACTNet: a frequency-domain-informed transformer for mmWave image computational super-resolution in the spatial

Yuduo Lin, Jianliang Hu, Heng Wu

    Optics Express
    |May 4, 2026
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    Summary

    Millimeter-wave (mmWave) imaging struggles with detail loss due to diffraction. Our frequency-aware network, FACTNet, enhances high-frequency details in mmWave security images for better object detection.

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    Area of Science:

    • Physics
    • Computer Science
    • Electrical Engineering

    Background:

    • Millimeter-wave (mmWave) imaging is crucial for security screening due to its non-ionizing and penetrative properties.
    • Diffraction in mmWave imaging acts as a low-pass filter, attenuating high-frequency details essential for identifying concealed objects.
    • Current deep learning super-resolution methods in the spatial domain are ineffective at restoring lost high-frequency information.

    Purpose of the Study:

    • To address the limitations of spatial domain super-resolution in mmWave imaging.
    • To develop a novel deep learning architecture capable of restoring high-frequency details lost due to diffraction.
    • To improve the visual fidelity and detail recovery in mmWave security screening.

    Main Methods:

    • Proposed the frequency-aware cross-attention transformer network (FACTNet).
    • Introduced the frequency-domain transformation module (FDTM) to adaptively filter and amplify high-frequency components in the Fourier domain.
    • Trained and evaluated FACTNet on a custom mmWave security dataset.

    Main Results:

    • FACTNet achieved state-of-the-art performance in mmWave image super-resolution.
    • Reconstructions exhibited superior visual fidelity and enhanced details compared to existing methods.
    • The proposed method demonstrated a significant reduction in image artifacts.

    Conclusions:

    • Aligning computational methods with the physical nature of image degradation is key to effective super-resolution.
    • FACTNet offers a principled and effective approach to overcoming diffraction-induced information loss in mmWave imaging.
    • This work advances the capabilities of mmWave security screening through improved computational reconstruction.