An Iteratively Reweighted Norm Algorithm for Total Variation Regularization
Total variation (TV) regularization has become a popular method for a wide variety of image restoration problems, including denoising and deconvolution. Recently, a number of authors have noted the advantages, including superior performance with certain non-Gaussian noise, of replacing the standard lscr2 data fidelity term with an lscr1 norm. We propose a simple but very flexible and computationally efficient method, the iteratively reweighted norm algorithm, for minimizing a generalized TV functional which includes both the lscr2-TV and and lscr2-TV problems.