A generalized vector-valued total variation algorithm

Authors:
Paul Rodríguez;Brendt Wohlberg
Affiliations:
Digital Signal Processing Group, Pontificia Universidad Católica del Perú, Lima, Peru;T-5 Applied Mathematics and Plasma Physics, Los Alamos National Laboratory, Los Alamos, NM
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 1

Iterative methods for total variation denoising

SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient minimization method for a generalized total variation functional

IEEE Transactions on Image Processing
Color TV: total variation methods for restoration of vector-valued images

IEEE Transactions on Image Processing
Deblurring of Color Images Corrupted by Impulsive Noise

IEEE Transactions on Image Processing
Efficient Total Variation Minimization Methods for Color Image Restoration

IEEE Transactions on Image Processing

Total variation regularization algorithms for images corrupted with different noise models: a review

Journal of Electrical and Computer Engineering

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Abstract

We propose a simple but flexible method for solving the generalized vector-valued TV (VTV) functional, which includes both the l2-VTV and l1-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 (l2-VTV case) and salt-and-pepper noise (l1-VTV case).