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Updated: Jul 3, 2026

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Dual-branch wavelet diffusion for single-pixel imaging.

Zhiqiang Wen, Qiu Tu, Qiurong Yan

    Optics Express
    |July 2, 2026
    PubMed
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    This study introduces WDNet, a novel wavelet-domain dual-path reconstruction method for single-pixel imaging. It enhances image clarity and reduces noise under ultra-low measurement rates by separating and processing high- and low-frequency components.

    Area of Science:

    • Computational imaging
    • Image reconstruction
    • Signal processing

    Background:

    • Single-pixel imaging (SPI) reconstructs images computationally using spatial light modulation and a single-point detector.
    • Existing SPI reconstruction methods struggle with high-frequency detail loss and noise at ultra-low measurement rates.

    Purpose of the Study:

    • To develop an advanced reconstruction method for single-pixel imaging under ultra-low measurement rates.
    • To address the limitations of current methods in preserving high-frequency details and mitigating noise.

    Main Methods:

    • Proposed WDNet, a wavelet-domain dual-path reconstruction approach.
    • Decomposed images into high-frequency and low-frequency components in the wavelet domain.
    • Reconstructed components separately using a lightweight network for details and a diffusion process for structure.

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    Last Updated: Jul 3, 2026

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    Main Results:

    • WDNet achieved clearer reconstruction results with reduced noise compared to existing methods.
    • The method demonstrated superior performance under extremely low measurement rates.
    • Effective recovery of high-frequency details and structural stability was observed.

    Conclusions:

    • WDNet offers a targeted approach to reconstruct images in single-pixel imaging by addressing different frequency components distinctly.
    • The method significantly improves image quality in challenging low-measurement-rate scenarios.
    • WDNet shows promise for practical applications of single-pixel imaging requiring high fidelity at low sampling rates.