An Iteratively Reweighted Norm Algorithm for Minimization of Total Variation Functionals
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. A number of authors have recently noted the advantages of replacing the standard lscr2data fidelity term with an lscr1 norm. We propose a simple but very flexible method for solving a generalized TV functional that includes both the lscr2 -TV and lscr1 -TV problems as special cases. This method offers competitive computational performance for lscr2 -TV and is comparable to or faster than any other lscr1 -TV algorithms of which we are aware.