Thursday, 8/3/2017 “A New Wavelet Denoising Method for Experimental Signals”
Madhur Srivastava and Jack H. Freed, ACERT (National Biomedical center for Advanced ESR Technology), Department of Chemistry and Chemical Biology, Cornell University
We have recently developed a new method to denoise experimental signals using wavelet transforms. The new method is superior to standard wavelet denoising methods. It has been applied to both cw and pulse electron-spin resonance (ESR) signals, and it is found to increase the SNR by about two orders of magnitude, while preserving the integrity of the signal. It is applicable to a wide variety of experimental signals. In addition, its computation time is more than six times faster than the earlier methods. The published signals that we denoised successfully are one-dimensional.[1,2] However, we are extending this approach to two (and three) dimensional signals such as multi-dimensional NMR and cryo-EM, where we expect to achieve comparable SNR improvement.
1. A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds. M. Srivastava, C.L Anderson, and J.H. Freed. IEEE Access 4(1), 3862-3877 (2016)
2. A New Wavelet Denoising Method of Experimental Time-Domain Signals: Pulsed Dipolar Electron Spin Resonance. M. Srivastava, E.R. Georgieva, and J.H. Freed. J. Phys. Chem. A. 121, 2453-2465 (2017)
3. Srivastava, M.; Anderson, C.L.; Freed, J.H. Systems, methods and programs for denoising signals using wavelets, U.S. Provisional Patent Application No. 62/334,626.