A generalized vector-valued total variation algorithm
Paul Rodríguez, Brendt E. Wohlberg · 2009
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).