@inproceedings{ade9e71b534b4d13b4598e2df724eb57,
title = "Color image deblurring with impulsive noise",
abstract = "We propose a variational approach for deblurring and impulsive noise removal in multi-channel images. A robust data fidelity measure and edge preserving regularization are employed. We consider several regularization approaches, such as Beltrami flow, Mumford-Shah and Total-Variation Mumford-Shah. The latter two methods are extended to multi-channel images and reformulated using the Γ- convergen ce approximation. Our main contribution is in the unification of image deblurring and impulse noise removal in a multi-channel variational framework. Theoretical and experimental results show that the Mumford-Shah and Total Variation Mumford Shah regularization methods are superior to other color image restoration regularizers. In addition, these two methods yield a denoised edge map of the image.",
author = "Leah Bar and Alexander Brook and Nir Sochen and Nahum Kiryati",
year = "2005",
doi = "10.1007/11567646_5",
language = "אנגלית",
isbn = "3540293485",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "49--60",
booktitle = "Variational, Geometric, and Level Set Methods in Computer Vision - Third International Workshop, VLSM 2005, Proceedings",
address = "גרמניה",
note = "3rd International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision, VLSM 2005 ; Conference date: 16-10-2005 Through 16-10-2005",
}