A generalized vector-valued total variation algorithm

We propose a simple but flexible method for solving the generalized vector-valued TV (VTV) functional, which includes both the ¿²-VTV and ¿¹-VTV regularizations as special cases, to address the problems of deconvolution and denoising of vector-valued (e.g. color) images with Gaussian or salt-and-pepper noise. This algorithm is the vectorial extension of the Iteratively Reweighted Norm (IRN) algorithm [1] originally developed for scalar (grayscale) images. This method offers competitive computational performance for denoising and deconvolving vector-valued images corrupted with Gaussian (¿²-VTV case) and salt-and-pepper noise (¿¹-VTV case).