Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review. (24th July 2013)

• Record Type: Journal Article
• Authors: Rodríguez, Paul
• Other Names: Luisier Florian Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2013(2013)
• Publication Date: 2013-07-24
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2013/217021
• Languages: English
• ISSNs: 2090-0147

Abstract

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 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.