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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.
Srivastava et al. (Fri,) studied this question.