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

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. © 2013 paul rodríguez.