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Journal of Electrical and Computer Engineering
Volume 2013, Article ID 217021, 18 pages
Review Article

Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review

Department of Electrical Engineering, Pontifical Catholic University of Peru, San Miguel, Lima 32, Peru

Received 26 October 2012; Revised 17 May 2013; Accepted 9 June 2013

Academic Editor: Florian Luisier

Copyright © 2013 Paul Rodríguez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model. We also include the description of the maximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem.