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Circuits, Systems, and Signal Processing

, Volume 37, Issue 9, pp 3903–3926 | Cite as

Sensitivity and Variability Analysis for Image Denoising Using Maximum Likelihood Estimation of Exponential Distribution

  • Amita Nandal
  • Arvind Dhaka
  • Hamurabi Gamboa-Rosales
  • Ninoslav Marina
  • Jorge I. Galvan-Tejada
  • Carlos E. Galvan-Tejada
  • Arturo Moreno-Baez
  • Jose M. Celaya-Padilla
  • Huizilopoztli Luna-Garcia
Article
  • 88 Downloads

Abstract

In this paper, we have performed denoising when the pixel values of images are corrupted by Gaussian and Poisson noises. This paper introduces a new class exponential distribution which lies between Poisson and Gamma distributions. The proposed method combines the ion for denoising the pixels and later a minimization using log-likelihood estimation is performed. The characteristic equation is based on various image parameters like mean, variance, mean deviation, distortion index, shape and scale parameters for minimizing the noise and for maximizing image edge strength to enhance overall visual quality of the image. By utilizing the exponential distribution, we can adaptively control the distortion in the image by minimizing Gaussian and Poisson noises in accordance with the image feature. The simulation results indicate that the proposed algorithm is very efficient to strengthen edge information and remove noise. To provide a probabilistic model we have used statistical approximation of mean and variances. Later, we have evaluated sensitivity and variability effect as well on the image restoration. Experiments were conducted on different test images, which were corrupted by different noise levels in order to assess the performance of the proposed algorithm in comparison with standard and other related denoising methods.

Keywords

Exponential distribution Gamma-distributed noise Image denoising Log-likelihood estimation Poisson-distributed noise Sensitivity Statistical estimation and variability 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Amita Nandal
    • 1
  • Arvind Dhaka
    • 2
  • Hamurabi Gamboa-Rosales
    • 3
  • Ninoslav Marina
    • 1
  • Jorge I. Galvan-Tejada
    • 3
  • Carlos E. Galvan-Tejada
    • 3
  • Arturo Moreno-Baez
    • 3
  • Jose M. Celaya-Padilla
    • 4
  • Huizilopoztli Luna-Garcia
    • 3
  1. 1.Universty of Information Science and TechnologyOhridMacedonia
  2. 2.National Institute of TechnologyHamirpurIndia
  3. 3.Faculty of Electrical EngineeringAutonomous University of ZacatecasZacatecasMexico
  4. 4.CONACYT - Autonomous University of ZacatecasZacatecasMexico

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