Title :
A generalized vector-valued total variation algorithm
Author :
Rodríguez, Paul ; Wohlberg, Brendt
Author_Institution :
Digital Signal Process. Group, Pontificia Univ. Catolica del Peru, Lima, Peru
Abstract :
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).
Keywords :
Gaussian noise; deconvolution; image denoising; Gaussian noise; computational performance; deconvolution; denoising; generalized vector-valued TV functional; generalized vector-valued total variation; grayscale images; iteratively reweighted norm; salt-and-pepper noise; scalar images; vector-valued images; Color; Colored noise; Gaussian noise; Gold; Gray-scale; Iterative algorithms; Laboratories; Noise reduction; Signal processing algorithms; TV; Color image processing; Vector-valued Total Variation;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5413587
