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Denoising Method for NV-Center Fluorescence Signals Based on MPA-VMD Combined with Wavelet Thresholding.

Yanxin He1, Xin Li1, Zhonghao Li1

  • 1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, School of Instrument and Electronics, North University of China, Taiyuan 030051, China.

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Summary
This summary is machine-generated.

A new hybrid denoising method enhances nitrogen-vacancy center signals by combining marine predators algorithm-optimized variational mode decomposition and wavelet thresholding. This improves signal-to-noise ratio and accuracy for quantum sensing applications.

Keywords:
ODMRfluorescence signal denoisingmarine predators algorithmnitrogen-vacancy centervariational mode decompositionwavelet thresholding

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Area of Science:

  • Quantum Information Science
  • Materials Science
  • Signal Processing

Background:

  • Nitrogen-vacancy (NV) centers in diamond are crucial for quantum sensing.
  • Acquiring clean fluorescence signals from NV centers is challenging due to complex noise.
  • Accurate optical detected magnetic resonance (ODMR) parameter estimation is vital for NV center applications.

Purpose of the Study:

  • To develop a hybrid denoising framework for improving NV center fluorescence signal quality.
  • To enhance the accuracy of ODMR resonance parameter estimation using the proposed denoising method.
  • To validate the synergistic effect of variational mode decomposition (VMD) and wavelet thresholding for noise reduction.

Main Methods:

  • Proposed a hybrid denoising framework integrating marine predators algorithm (MPA)-optimized VMD and wavelet thresholding.
  • MPA adaptively optimizes VMD parameters for reliable signal decomposition.
  • Wavelet thresholding selectively removes noise from intrinsic mode functions while preserving signal integrity.

Main Results:

  • Achieved a signal-to-noise ratio (SNR) of 57.12 dB, a 14.6% improvement over standalone VMD.
  • Reduced Root Mean Square Error (RMSE) by 56.7% and improved SNR by 7.9% compared to wavelet thresholding alone.
  • Significantly improved ODMR parameter estimation accuracy: reduced center frequency RMSE by 29.8% and FWHM RMSE by 44.5% compared to wavelet denoising.

Conclusions:

  • The hybrid MPA-VMD and wavelet thresholding framework effectively reduces noise in NV center fluorescence signals.
  • The method offers superior performance in ODMR parameter estimation, particularly for Full Width at Half Maximum (FWHM).
  • This approach has significant potential for high-precision quantum sensing and precision measurement applications.