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    Frequency-domain photoacoustic microscopy (FD-PAM) struggles with low signal-to-noise ratio (SNR). A U-Net neural network enhances FD-PAM images, improving accessibility and applicability without high power or averaging.

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    Area of Science:

    • Biomedical Imaging
    • Optical Microscopy
    • Signal Processing

    Background:

    • Frequency-domain photoacoustic microscopy (FD-PAM) offers cost-efficient imaging.
    • FD-PAM suffers from significantly lower signal-to-noise ratio (SNR) compared to time-domain systems.
    • Low SNR limits FD-PAM's practical applications and requires extensive signal averaging or high optical power.

    Purpose of the Study:

    • To address the inherent SNR limitations of FD-PAM.
    • To improve the image quality and accessibility of FD-PAM systems.
    • To expand the applicability of FD-PAM to more demanding imaging scenarios.

    Main Methods:

    • Utilized a U-Net neural network for image augmentation.
    • Implemented intensity-modulated laser beams for single-frequency photoacoustic wave excitation.
    • Focused on enhancing SNR without increasing optical power or averaging.

    Main Results:

    • Achieved significant SNR improvement in FD-PAM images.
    • Demonstrated effective image augmentation using the U-Net architecture.
    • Maintained high image quality standards despite SNR enhancement.

    Conclusions:

    • The U-Net approach effectively overcomes FD-PAM's SNR limitations.
    • This method reduces system cost and expands the utility of photoacoustic microscopy.
    • Improved FD-PAM accessibility and applicability for advanced imaging needs.