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-andpepper 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).

• Type: Journal
• Year: 2009
• Where: ICIP
• Authors: Paul Rodriguez, Brendt Wohlberg
• Keywords: -VTV Case, Competitive Computational Performance, Generalized Vector-valued Tv, ICIP 2009, Image Processing