TY - GEN
T1 - A variational framework for non-local image inpainting
AU - Arias, Pablo
AU - Caselles, Vicent
AU - Sapiro, Guillermo
PY - 2009
Y1 - 2009
N2 - Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for the problem of non-local image inpainting, from which several previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.
AB - Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for the problem of non-local image inpainting, from which several previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.
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U2 - 10.1007/978-3-642-03641-5_26
DO - 10.1007/978-3-642-03641-5_26
M3 - Conference contribution
AN - SCOPUS:70350597534
SN - 3642036406
SN - 9783642036408
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 345
EP - 358
BT - Energy Minimization Methods in Computer Vision and Pattern Recognition - 7th International Conference, EMMCVPR 2009, Proceedings
T2 - 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2009
Y2 - 24 August 2009 through 27 August 2009
ER -