Srivastava Madhur, Anderson C Lindsay, Freed Jack H
National Biomedical Center for Advanced ESR Technology (ACERT) and the Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, 14853 USA.
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853 USA.
IEEE Access. 2016;4:3862-3877. doi: 10.1109/ACCESS.2016.2587581. Epub 2016 Jul 7.
A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of- the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method 1) provides a method to select the number of decomposition levels to denoise, 2) uses a new formula to calculate noise thresholds that does not require noise estimation, 3) uses separate noise thresholds for positive and negative wavelet coefficients, 4) applies denoising to the Approximation component, and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance (cw-ESR) spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. Also, its computation time is more than 6 times faster.
提出了一种使用小波变换对一维实验信号进行去噪的新方法。尽管当前最先进的小波去噪方法比其他去噪方法表现更好，但它们对实验信号的效果并不十分理想。与图像和其他信号不同，例如化学和生物物理应用中的实验信号，对标准小波去噪方法引起的信号失真和去噪不足的容忍度较低。新方法：1）提供了一种选择去噪分解层数的方法；2）使用一种无需噪声估计的新公式来计算噪声阈值；3）对正负小波系数使用单独的噪声阈值；4）对近似分量进行去噪；5）允许灵活调整噪声阈值。将新方法应用于连续波电子自旋共振（cw-ESR）光谱，发现它能在不使信号失真的情况下将信噪比（SNR）提高超过32 dB，而标准去噪方法将SNR提高不到10 dB且会产生一些失真。此外，其计算时间快6倍以上。
