SIGNAL DENOISING BASED ON QUANTUM ADAPTIVE TRANSFORMATION AND SINGULAR VALUE DECOMPOSITION
Keywords:
Denoising algorithm, Quantum adaptive transformation, Singular value decomposition, Spectral dataAbstract
The denoising algorithm plays an important role in biomedical signal processing such as spectrophotometry, and is an important means to improve signal quality and detection accuracy. This article combines singular value decomposition denoising and quantum adaptive transformation to process Gaussian spectral data. The results indicate that denoising evaluation indicators such as signal-to-noise ratio, mean square error, and normalized cross-correlation coefficient have all improved. Moreover, the stronger the noise, the more obvious the advantages of the proposed algorithm compared to other denoising algorithms. And this method can be extended to pre-processing biomedical signals, such as electrocardiograms and electroencephalograms.References
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