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

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

scholarly journals Blind estimation of white Gaussian noise variance in highly textured images

2018 ◽  
Vol 2018 (13) ◽  
pp. 382-1-382-5 ◽  
Author(s):  
Mykola Ponomarenko ◽  
Nikolay Gapon ◽  
Viacheslav Voronin ◽  
Karen Egiazarian
Keyword(s):  
Gaussian Noise ◽  
White Gaussian Noise ◽  
Blind Estimation ◽  
Noise Variance

Blind evaluation of additive noise variance in textured images by nonlinear processing of block DCT coefficients

2003 ◽  
Author(s):  
Nikolay N. Ponomarenko ◽  
Vladimir V. Lukin ◽  
Sergey K. Abramov ◽  
Karen O. Egiazarian ◽  
Jaakko T. Astola
Keyword(s):  
Additive Noise ◽  
Noise Variance ◽  
Nonlinear Processing ◽  
Dct Coefficients ◽  
Block Dct

scholarly journals Data Denoising By Noise Invalidation

2021 ◽  
Author(s):  
Nima Nikvand
Keyword(s):  
Additive Noise ◽  
Variance Estimation ◽  
Additive White Gaussian Noise ◽  
Visual Quality ◽  
Noise Variance ◽  
Soft Thresholding ◽  
New Methods ◽  
Shrinkage Methods ◽  
Similarities And Differences ◽  
Non Gaussian

In this thesis, the problem of data denoising is studied, and two new denoising approaches are proposed. Using statistical properties of the additive noise, the methods provide adaptive data-dependent soft thresholding techniques to remove the additive noise. The proposed methods, Point-wise Noise Invlaidating Soft Thresholding (PNIST) and Accumulative Noise Invalidation Soft Thresholding (ANIST), are based on Noise Invalidation. The invalidation exploits basic properties of the additive noise in order to remove the noise effects as much as possible. There are similarities and differences between ANIST and PNIST. While PNIST performs better in the case of additive white Gaussian noise, ANIST can be used with both Gaussian and non Gaussian additive noise. As part of a data denoising technique, a new noise variance estimation is also proposed. The thresholds proposed by NIST approaches are comparable to the shrinkage methods, and our simulation results promise that the new methods can outperform the existing approaches in various applications. We also explore the area of image denoising as one of the main applications of data denoising and extend the proposed approaches to two dimensional applications. Simulations show that the proposed methods outperform common shrinkage methods and are comparable to the famous BayesShrink method in terms of Mean Square Error and visual quality.

Improved minimal inter-quantile distance method for blind estimation of noise variance in images

2007 ◽  
Author(s):  
Vladimir V. Lukin ◽  
Sergey K. Abramov ◽  
Alexander A. Zelensky ◽  
Jaakko T. Astola ◽  
Benoit Vozel ◽  
...  
Keyword(s):  
Blind Estimation ◽  
Noise Variance ◽  
Distance Method

A fast parallel algorithm for blind estimation of noise variance

1990 ◽  
Vol 12 (2) ◽  
pp. 216-223 ◽  
Author(s):  
P. Meer ◽  
J.-M. Jolion ◽  
A. Rosenfeld
Keyword(s):  
Parallel Algorithm ◽  
Blind Estimation ◽  
Noise Variance

BLIND ESTIMATION OF NOISE VARIANCE FOR 1D SIGNAL DENOISING

2020 ◽  
Vol 79 (7) ◽  
pp. 567-581
Author(s):  
A. Kharkov ◽  
V. Oliinyk ◽  
V. V. Lukin ◽  
S. S. Krivenko
Keyword(s):  
Signal Denoising ◽  
Blind Estimation ◽  
Noise Variance
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