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

To address complex noise in nitrogen-vacancy center fluorescence signal acquisition, a hybrid denoising framework combining marine predators algorithm-optimized variational mode decomposition (VMD) and wavelet thresholding is proposed. MPA adaptively selects VMD parameters, enhancing decomposition reliability. Wavelet thresholding then suppresses noise-dominant intrinsic mode functions while preserving signal components. Results show significant SNR improvement to 57.12 dB (14.6% higher than standalone VMD), RMSE reduction by 56.7%, and 7.9% SNR enhancement over wavelet thresholding alone, with the correlation coefficient reaching 0.97. More importantly, the proposed method substantially improves the accuracy of ODMR resonance parameter estimation. Compared to wavelet denoising, RMSE of the center frequency is reduced by 29.8% and RMSE of the FWHM is reduced by 44.5%; compared to VMD denoising, the FWHM RMSE is reduced by 20.7% while maintaining comparable center frequency accuracy. This approach validates the synergistic effect of VMD's global decomposition and wavelet's local denoising, offering an effective method for high-precision ODMR inversion with substantial application potential in quantum sensing and precision measurement.