TY - GEN
T1 - A novel framework for nonlocal vectorial total variation based on ℓp,Q,R-norms
AU - Duran, Joan
AU - Moeller, Michael
AU - Sbert, Catalina
AU - Cremers, Daniel
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - In this paper, we propose a novel framework for restoring color images using nonlocal total variation (NLTV) regularization. We observe that the discrete local and nonlocal gradient of a color image can be viewed as a 3D matrix/or tensor with dimensions corresponding to the spatial extend, the differences to other pixels, and the color channels. Based on this observation we obtain a new class of NLTV methods by penalizing the ℓp,q,r norm of this 3D tensor. Interestingly, this unifies several local color total variation (TV) methods in a single framework. We show in several numerical experiments on image denoising and deblurring that a stronger coupling of different color channels – particularly, a coupling with the ℓ∞ norm – yields superior reconstruction results.
AB - In this paper, we propose a novel framework for restoring color images using nonlocal total variation (NLTV) regularization. We observe that the discrete local and nonlocal gradient of a color image can be viewed as a 3D matrix/or tensor with dimensions corresponding to the spatial extend, the differences to other pixels, and the color channels. Based on this observation we obtain a new class of NLTV methods by penalizing the ℓp,q,r norm of this 3D tensor. Interestingly, this unifies several local color total variation (TV) methods in a single framework. We show in several numerical experiments on image denoising and deblurring that a stronger coupling of different color channels – particularly, a coupling with the ℓ∞ norm – yields superior reconstruction results.
UR - http://www.scopus.com/inward/record.url?scp=84921860174&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14612-6_11
DO - 10.1007/978-3-319-14612-6_11
M3 - Conference contribution
AN - SCOPUS:84921860174
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 141
EP - 154
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 10th International Conference,EMMCVPR 2015, Proceedings
A2 - Tai, Xue-Cheng
A2 - Bae, Egil
A2 - Chan, Tony F.
A2 - Lysaker, Marius
PB - Springer Verlag
T2 - 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015
Y2 - 13 January 2015 through 16 January 2015
ER -