Multiplicative noise variance evaluation in mm-band radar images using myriad estimation

2002 ◽  
Author(s):  
S.K. Abramov ◽  
V.V. Lukin ◽  
A.A. Zelensky
Keyword(s):  
Multiplicative Noise ◽  
Noise Variance ◽  
Radar Images

Methods for Blind Evaluation of Noise Variance in Multichannel Optical and Radar Images

2006 ◽  
Vol 65 (6) ◽  
pp. 527-556 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
N. N. Ponomarenko ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Noise Variance ◽  
Radar Images

A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

1982 ◽  
Vol PAMI-4 (2) ◽  
pp. 157-166 ◽  
Author(s):  
Victor S. Frost ◽  
Josephine Abbott Stiles ◽  
K. S. Shanmugan ◽  
Julian C. Holtzman
Keyword(s):  
Multiplicative Noise ◽  
Digital Filtering ◽  
Radar Images

A Nonmodel Dual-Tree Wavelet Thresholding for Image Denoising Through Noise Variance Optimization Based on Improved Chaotic Drosophila Algorithm

2017 ◽  
Vol 31 (08) ◽  
pp. 1754015 ◽  
Author(s):  
Lin Zhang ◽  
Xiaomou Zhou ◽  
Zhongbin Wang ◽  
Chao Tan ◽  
Xinhua Liu
Keyword(s):  
Wavelet Transform ◽  
Multiplicative Noise ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Model ◽  
Mining Machine ◽  
Discrete Wavelet ◽  
Wavelet Thresholding ◽  
Noise Variance ◽  
Shrinkage Function

To remove image noise without considering the noise model, a dual-tree wavelet thresholding method (CDOA-DTDWT) is proposed through noise variance optimization. Instead of building a noise model, the proposed approach using the improved chaotic drosophila optimization algorithm (CDOA), to estimate the noise variance, and the estimated noise variance is utilized to modify wavelet coefficients in shrinkage function. To verify the optimization ability of the improved CDOA, the comparisons with basic DOA, GA, PSO and VCS are performed as well. The proposed method is tested to remove addictive noise and multiplicative noise, and denoising results are compared with other representative methods, e.g. Wiener filter, median filter, discrete wavelet transform-based thresholding (DWT), and nonoptimized dual-tree wavelet transform-based thresholding (DTDWT). Moreover, CDOA-DTDWT is applied as pre-processing utilization for tracking roller of mining machine as well. The experiment and application results prove the effectiveness and superiority of the proposed method.

Joint Blind Estimation of Channel Length and Noise Variance in OFDM Systems

2011 ◽  
Vol E94-B (12) ◽  
pp. 3614-3617
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU
Keyword(s):  
Channel Length ◽  
Blind Estimation ◽  
Ofdm Systems ◽  
Noise Variance

A METHOD FOR AUTOMATIC BLIND ESTIMATION OF ADDITIVE NOISE VARIANCE IN DIGITAL IMAGES

2010 ◽  
Vol 69 (19) ◽  
pp. 1681-1702
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
A. V. Popov ◽  
P. Ye. Eltsov ◽  
Benoit Vozel ◽  
...  
Keyword(s):  
Additive Noise ◽  
Digital Images ◽  
Blind Estimation ◽  
Noise Variance

AUTOMATIC ESTIMATION OF SPATIALLY CORRELATED NOISE VARIANCE IN SPECTRAL DOMAIN FOR IMAGES

2014 ◽  
Vol 73 (6) ◽  
pp. 511-527 ◽  
Author(s):  
V.V. Abramova ◽  
S. K. Abramov ◽  
V. V. Lukin ◽  
A. A. Roenko ◽  
Benoit Vozel
Keyword(s):  
Spectral Domain ◽  
Correlated Noise ◽  
Noise Variance ◽  
Spatially Correlated ◽  
Automatic Estimation ◽  
Spatially Correlated Noise

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

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