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    Area of Science:

    • Atmospheric optics
    • Signal processing
    • Remote sensing technology

    Background:

    • Atmospheric turbulence affects lidar measurements.
    • Accurate atmospheric coherence length is crucial for optical systems.
    • Existing denoising methods may compromise data integrity.

    Purpose of the Study:

    • To propose and validate a novel denoising method for atmospheric lidar data.
    • To improve the accuracy of atmospheric coherence length measurements.
    • To enhance the inference of refractive index structure constant (C n2).

    Main Methods:

    • Combining wavelet decomposition (WD) for noise reduction.
    • Implementing an adaptive median filter (ADMF) for signal enhancement.
    • Verifying the WD-ADMF method through simulations and real-world lidar measurements.

    Main Results:

    • The WD-ADMF method significantly improves the average peak signal-to-noise ratio (PSNR).
    • Reduced centroid error demonstrates enhanced data accuracy.
    • Maintained data integrity ensures reliable coherence length measurements.

    Conclusions:

    • The WD-ADMF method effectively denoises atmospheric lidar data.
    • This approach leads to more accurate measurements of atmospheric coherence length.
    • Improved data quality facilitates reliable inference of C n2 from coherence length.