Robust image signal-to-noise ratio estimation using mixed lagrange time delay estimation autoregressive model

Scanning ◽  
2006 ◽  
Vol 26 (6) ◽  
pp. 287-295 ◽  
Author(s):  
K. S. Sim ◽  
H. T. Chuah ◽  
Zheng Cheng
Keyword(s):  
Time Delay ◽  
Autoregressive Model ◽  
Signal To Noise Ratio ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Robust Image ◽  
Noise Ratio

scholarly journals Time Delay Estimation Method Used Median Filter in Low Signal to Noise Ratio Pulse Environment

2021 ◽  
Vol 252 ◽  
pp. 02039
Author(s):  
Hang Liu ◽  
Wenhong Liu
Keyword(s):  
Signal Processing ◽  
Time Delay ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Estimation Algorithm ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
P Value ◽  
Signal To Noise ◽  
Second Order Statistics

In practice, the collected signal often contains impulsive noise. The classical time delay estimation algorithm based on the second-order statistics of Gaussian distribution will degrade or even be unreliable, so that it cannot be used. Although the fractional low-order signal processing method can be better adapted to signal processing in the impulse noise environment, the determination of the order p value of the fractional low-order moment depends on the prior knowledge or estimation of the characteristic index α value of the pulse, and when the pulse is stronger or the signal-to-noise ratio is low, the performance cannot meet the requirements well. The paper adopted the method of median filter preprocessing. First, the abnormal points (pulse points) are removed in the noise and return the noise to the Gaussian model distribution; next, use the time delay estimation algorithm under the second-order statistics to avoid the estimate of p-value. Computer simulation experiments show that the method proposed in this paper has better estimation performance in low snr pulse environment.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio
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