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    A new optical sectioning method improves continuous vertical scanning structured illumination microscopy (CVS-SIM) for complex surfaces. This technique enhances signal-to-noise ratio and reduces reconstruction error, enabling high-quality surface topography measurement.

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    Area of Science:

    • Optical Metrology
    • Surface Characterization
    • Microscopy Techniques

    Background:

    • Continuous vertical scanning structured illumination microscopy (CVS-SIM) offers efficiency but struggles with complex surfaces.
    • Challenges include low signal-to-noise ratio (SNR) and poor reconstruction accuracy due to background fluctuations and varying surface properties.

    Purpose of the Study:

    • To develop a robust optical sectioning method for CVS-SIM to overcome limitations in complex surface topography reconstruction.
    • To enhance measurement accuracy and SNR for challenging samples.

    Main Methods:

    • Integration of sliding-window background estimation with Hilbert transform-based contrast-weighted HiLo image fusion.
    • Utilized adaptive filtering for background intensity estimation and noise suppression.
    • Employed Hilbert transform for signal separation and fringe contrast weighting for fusion.

    Main Results:

    • Achieved a higher SNR in axial modulation response for samples with steep slopes, reducing reconstruction error from 1.458 µm to 0.104 µm.
    • Maintained measurement accuracy and lateral resolution on complex textures and reflectivity variations, suppressing noise by over 51%.
    • Demonstrated consistent high-quality surface reconstruction across diverse topographies.

    Conclusions:

    • The proposed method significantly improves the performance of CVS-SIM on complex surfaces.
    • It offers a robust solution for accurate and high-SNR surface topography reconstruction in challenging metrology applications.