GHCTWNNM: a gradient histogram constraint truncated WNNM denoising algorithm for LIBS with spectrum-to-image conversion

Abstract

Laser-induced breakdown spectroscopy (LIBS) technology has been widely applied across various fields due to its rapid and straightforward analytical capabilities. However, this technology is susceptible to noise interference during the detection process, which will seriously affect the quantitative analysis accuracy. To mitigate the influence of noise and improve the analysis accuracy, we propose a Gradient Histogram Constraint Truncated Weighted Nuclear Norm Minimization (GHCTWNNM) algorithm for LIBS spectra denoising. Here, we innovatively convert the denoising problem of 1D spectra data into a 2D image denoising problem, where we can take advantage of the superior image denoising technology to enhance the denoising effect of LIBS spectra. On the basis of the traditional WNNM algorithm, we introduce the truncation threshold and gradient histogram constraints, which not only improve the computational efficiency but also prevent distortion issues caused by excessive smoothing of image texture details. Subsequently, we derived the solution of the GHCTWNNM algorithm using the Alternating Direction Method of Multipliers (ADMM) method. The experimental results demonstrate that the GHCTWNNM algorithm achieves a remarkable improvement in denoising performance, with an increase of approximately 6 dB in ΔSNR compared to the WNNM algorithm. Moreover, in comparison with nine other image denoising algorithms, GHCTWNNM not only delivers superior denoising capabilities but also exhibits greater adaptability to different noise environments, especially in a high background noise environment. Additionally, the R² of the Al element quantitative analysis result has increased by 0.26 after applying the GHCTWNNM denoising method. In summary, the LIBS denoising method based on the GHCTWNNM algorithm can effectively enhance the spectra SNR and significantly reduce the errors in quantitative analysis caused by noise, thereby enhancing the accuracy and reliability of LIBS. This provides a strong basis for its wide application and further development in various related fields.