Total variation regularization algorithms for images corrupted with different noise models: a review

Authors:
Paul Rodríguez
Affiliations:
Department of Electrical Engineering, Pontifical Catholic University of Peru, Lima, Peru
Venue:
Journal of Electrical and Computer Engineering
Year:
2013

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Abstract

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 impulsemodel. We also include the description of themaximum 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.