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.