[IPOL announce] new article: On the Implementation of Collaborative TV Regularization: Application to Cartoon+Texture Decomposition

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2016/141/

Joan Duran, Michael Moeller, Catalina Sbert, and Daniel Cremers,
On the Implementation of Collaborative TV Regularization: Application to 
Cartoon+Texture Decomposition,
Image Processing On Line, 6 (2016), pp. 27–74.
http://dx.doi.org/10.5201/ipol.2016.141

Abstract
This paper deals with the analysis, implementation, and comparison of 
several vector-valued total variation (TV) methods that extend the 
Rudin-Osher-Fatemi variational model to color images. By considering the 
discrete gradient of a multichannel image as a 3D structure matrix with 
dimensions corresponding to the spatial extend, the differences to other 
pixels and the color channels, we introduce in [J. Duran, M. Moeller, C. 
Sbert, and D. Cremers, 'Collaborative Total Variation: A General 
Framework for Vectorial TV Models', SIAM Journal on Imaging Sciences, 
9(1), pp.116-151, 2016] collaborative sparsity enforcing norms for 
penalizing the resulting tensor. We call this class of regularizations 
collaborative total variation (CTV). We first analyze the denoising 
properties of each collaborative norm for suppressing color artifacts 
while preserving image features and aligning edges. We then describe the 
primal-dual hybrid gradient method for solving the minimization problem 
in detail. The resulting CTV–L2 variational model can successfully be 
applied to many image processing tasks. On the one hand, an extensive 
performance comparison of several collaborative norms for color image 
denoising is provided. On the other hand, we analyze the ability of 
different CTV methods for decomposing a multichannel image into a 
cartoon and a textural part. Finally, we also include a short discussion 
on alternative minimization methods and compare their computational 
efficiency.