Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary...

Area of Science:

• Spectroscopy
• Signal Processing
• Data Analysis

Background:

• Spectral signals are susceptible to noise during acquisition and transmission.
• Effective signal denoising is crucial for accurate data interpretation, especially when signal and noise characteristics are unknown.
• Wavelet transformation is a powerful tool for signal denoising.

Purpose of the Study:

• To propose a novel noise reduction technique for laser-induced breakdown spectroscopy (LIBS) signals.
• To enhance the quality of LIBS data by minimizing noise distortions.
• To evaluate the effectiveness of the proposed denoising method.

Main Methods:

• Utilizing wavelet transform for signal denoising.
• Implementing an extension of Donoho's scheme with redundant wavelet transformation.
• Employing an adaptive threshold estimation method for noise suppression.

Main Results:

• Demonstrated successful noise reduction in both artificial and real LIBS signals.
• Showcased effective denoising even with varying noise intensities.
• Presented a comparative analysis against alternative and traditional denoising techniques.

Conclusions:

• The proposed wavelet transform-based noise reduction technique is effective for LIBS signals.
• The method successfully suppresses noise while preserving essential signal features.
• This approach offers a valuable improvement over existing denoising strategies for spectroscopic data.