An automatic glucose monitoring signal denoising method with noise level estimation and responsive filter updating

2018 ◽  
Vol 41 ◽  
pp. 172-185 ◽  
Author(s):  
Hong Zhao ◽  
Chunhui Zhao ◽  
Furong Gao
Keyword(s):  
Noise Level ◽  
Glucose Monitoring ◽  
Signal Denoising ◽  
Denoising Method ◽  
Noise Level Estimation

Rapid Blind Denoising Method for Grating Fringe Images Based on Noise Level Estimation

2021 ◽  
pp. 1-1
Author(s):  
Shenhua Zhang ◽  
Yanxi Yang ◽  
Qiaomeng Qin ◽  
Lianqiang Feng ◽  
Licong Jiao
Keyword(s):  
Noise Level ◽  
Denoising Method ◽  
Noise Level Estimation

Noise level estimation method with application to EMD-based signal denoising

2016 ◽  
Vol 27 (4) ◽  
pp. 763-771 ◽  
Author(s):  
Li Xiaoyu ◽  
◽  
Jin Jing ◽  
Shen Yi ◽  
Liu Yipeng ◽  
...  
Keyword(s):  
Noise Level ◽  
Estimation Method ◽  
Signal Denoising ◽  
Noise Level Estimation

scholarly journals Sparse Representation Denoising for Radar High Resolution Range Profiling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Min Li ◽  
Gongjian Zhou ◽  
Bin Zhao ◽  
Taifan Quan
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Automatic Target Recognition ◽  
Time Shift ◽  
Denoising Method ◽  
Redundant Dictionary ◽  
Noise Level Estimation

Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

A novel segmentation based video-denoising method with noise level estimation

2014 ◽  
Vol 281 ◽  
pp. 507-520 ◽  
Author(s):  
Yang Cao ◽  
Shi Jie Zhang ◽  
Zheng Jun Zha ◽  
Jing Zhang ◽  
Chang Wen Chen
Keyword(s):  
Noise Level ◽  
Denoising Method ◽  
Noise Level Estimation ◽  
Video Denoising

Analysis of ECG signal denoising method based on S-transform

IRBM ◽  
2013 ◽  
Vol 34 (6) ◽  
pp. 362-370 ◽  
Author(s):  
M.K. Das ◽  
S. Ari
Keyword(s):  
Signal Denoising ◽  
Ecg Signal ◽  
Denoising Method ◽  
S Transform
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