Improved Method for Blind Estimation of the Variance of Mixed Noise Using Weighted LMS Line Fitting Algorithm

Sergey Abramov; Victoriya Zabrodina; Vladimir Lukin; Benoit Vozel; Kacem Chehdi; Jaakko Astola

Improved Method for Blind Estimation of the Variance of Mixed Noise Using Weighted LMS Line Fitting Algorithm

Sergey Abramov Victoriya Zabrodina Vladimir Lukin Benoit Vozel ¹ Kacem Chehdi ¹ Jaakko Astola

1 IETR - Institut d'Electronique et de Télécommunications de Rennes

Abstract : The paper addresses blind evaluation of the parameters of mixed noise in images. The conventional approach is based on line fitting in the scatter-plot of local variance estimates using LMS algorithm. This does not utilize the fact that the points in the scatter pot typically appear in clusters that depend on the image. It is shown that the use of weighted LMS algorithm that takes into account the number of points in clusters provides considerable improvement in the accuracy of line fitting and, thus, better estimation of the parameters of mixed noise.

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Conference papers

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https://hal.archives-ouvertes.fr/hal-00947031
Contributor : Yolande Sambin <>
Submitted on : Friday, February 14, 2014 - 3:25:56 PM
Last modification on : Friday, November 16, 2018 - 1:37:25 AM

Improved Method for Blind Estimation of the Variance of Mixed Noise Using Weighted LMS Line Fitting Algorithm

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Improved Method for Blind Estimation of the Variance of Mixed Noise Using Weighted LMS Line Fitting Algorithm

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