Wavelet dual-threshold denoising method based on interlayer correlation coefficient

Abstract

The invention provides a wavelet dual-threshold denoising method based on an interlayer correlation coefficient, which comprises the following steps of: processing a noisy signal by adopting stationary wavelet transform (SWT) to obtain an output coefficient of a low-frequency part and an output coefficient of a high-frequency part, and according to each group of lower threshold regulation factors represented by each firefly, processing the output coefficient of the high-frequency part by adopting a wavelet dual-threshold function based on an interlayer correlation coefficient, then reconstructing the processed output coefficient of the high-frequency part and the processed output coefficient of the low-frequency part to obtain a denoised signal, and finally taking the signal-to-noise ratio of the denoised signal as the brightness of fireflies. The higher the signal-to-noise ratio is, the higher the brightness of the fireflies is, the better the denoising effect of the lower threshold adjustment factor represented by each firefly is, and the optimal denoising signal is obtained by using the firefly algorithm. According to the method, a layered threshold value and a semi-soft threshold value method are combined, the firefly algorithm is added, and given threshold values are optimized through a signal-to-noise ratio (SNR) index; and the threshold value precision is improved.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a wavelet double-threshold denoising method based on interlayer correlation coefficients. Background technique [0002] In daily life, in order to obtain the required information, it is often necessary to collect relevant signals. However, the acquired signal will inevitably be disturbed by noise. This will reduce the quality of the collected signal, bring inconvenience to signal processing and analysis, and even cause errors. At present, in the field of denoising, wavelet theory is favored by many scholars because of its special advantages. They use wavelet to denoise and achieve very good results. The classic wavelet denoising methods include modulus maximum principle denoising method, correlation denoising method, wavelet threshold denoising method and translation invariant wavelet denoising method. Among them, the threshold denoising method is most widely used in...

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Problems solved by technology

[0004] In China, in recent years, many scholars have also carried out in-depth research in this area, but there is no relevant literature research on the optimization of threshold selection from the signal denoising index (such as signal-to-noise ratio), but this will affect to the accuracy of the selected threshold, thus affecting the denoising effect of the signal

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