Low rank priors for color image regularization
Möllenhoff T, Strekalovskiy E, Moeller M, Cremers D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 8932
Pages Range: 126-140
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Hong Kong, CHN
ISBN: 9783319146119
DOI: 10.1007/978-3-319-14612-6_10
Abstract
In this work we consider the regularization of vectorial data such as color images. Based on the observation that edge alignment across image channels is a desirable prior for multichannel image restoration, we propose a novel scheme of minimizing the rank of the image Jacobian and extend this idea to second derivatives in the framework of total generalized variation. We compare the proposed convex and nonconvex relaxations of the rank function based on the Schatten-q norm to previous color image regularizers and show in our numerical experiments that they have several desirable properties. In particular, the nonconvex relaxations lead to better preservation of discontinuities. The efficient minimization of energies involving nonconvex and nonsmooth regularizers is still an important open question. We extend a recently proposed primal-dual splitting approach for nonconvex optimization and show that it can be effectively used to minimize such energies. Furthermore, we propose a novel algorithm for efficiently evaluating the proximal mapping of the ℓq norm appearing during optimization. We experimentally verify convergence of the proposed optimization method and show that it performs comparably to sequential convex programming.
Involved external institutions
Germany (DE)How to cite
APA:
Möllenhoff, T., Strekalovskiy, E., Moeller, M., & Cremers, D. (2015). Low rank priors for color image regularization. In Xue-Cheng Tai, Egil Bae, Tony F. Chan, Marius Lysaker (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 126-140). Hong Kong, CHN: Springer Verlag.
MLA:
Möllenhoff, Thomas, et al. "Low rank priors for color image regularization." Proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015, Hong Kong, CHN Ed. Xue-Cheng Tai, Egil Bae, Tony F. Chan, Marius Lysaker, Springer Verlag, 2015. 126-140.
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