NDLI: Denoising method for capillary electrophoresis signal via learned tight frame

Content Provider	IET Digital Library
Author	Lu, Yixiang Wang, Zhenya Gao, Qingwei Sun, Dong Bao, Hua
Abstract	Since capillary electrophoresis (CE) signals are always contaminated by random noise, which has negative influence on the accuracy of detection and analysis, it is necessary to remove noise before further applications of the CE signals. In this study, a tight frame learned from the data itself is applied to the removal of noise for CE signals. To achieve an effective decomposition of the CE signal, a one-dimensional discrete tight frame tailored to the input signal is first constructed by introducing tight frame constraint into the popular dictionary learning model. Then, due to each subband containing different information of the noise, an adaptive threshold is computed to shrink the detail coefficients instead of using a global threshold. Finally, the denoised CE signal is reconstructed from the thresholded coefficients by using the inverse transform of the tight frame. To evaluate the denoising efficiency, the proposed method is applied to the simulated CE signals and real CE signals. Experimental results indicate that compared with other denoising methods, the proposed method obtains a better shape preservation of the peaks as well as a higher signal-to-noise ratio.
Starting Page	189
Ending Page	198
Page Count	10
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 4, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0242
Journal	IET Signal Processing
Publisher Date	2020-02-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximation Theory Capillary Electrophoresis Signal Denoised CE Signal Dictionary Learning Model Digital Signal Processing Electrophoresis Input Signal Integral Transforms in Numerical Analysis Interpolation And Function Approximation Inverse Transforms Knowledge Engineering Technique Learned Tight Frame Learning in AI Numerical Analysis One-dimensional Discrete Tight Frame Random Noise Signal Denoising Signal Processing And Detection Simulated CE Signal Tight Frame Constraint
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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