@inproceedings{bfbc6ca709994ac9a28c9394cdbd2b90,
title = "Nonlinear spectral image fusion",
abstract = "In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks. The well-localized and edge-preserving spectral TV decomposition allows to select frequencies of a certain image to transfer particular features, such as wrinkles in a face, from one image to another. We illustrate the effectiveness of the proposed approach in several numerical experiments, including a comparison to the competing techniques of Poisson image editing, linear osmosis, wavelet fusion and Laplacian pyramid fusion. We conclude that the proposed spectral TV image decomposition framework is a valuable tool for semi- and fully automatic image editing and fusion.",
keywords = "Image composition, Image fusion, Multiscale methods, Nonlinear spectral decomposition, Total variation regularization",
author = "Martin Benning and Michael M{\"o}ller and Nossek, \{Raz Z.\} and Martin Burger and Daniel Cremers and Guy Gilboa and Sch{\"o}nlieb, \{Carola Bibiane\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017 ; Conference date: 04-06-2017 Through 08-06-2017",
year = "2017",
doi = "10.1007/978-3-319-58771-4\_4",
language = "الإنجليزيّة",
isbn = "9783319587707",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "41--53",
editor = "Francois Lauze and Yiqiu Dong and Dahl, \{Anders Bjorholm\}",
booktitle = "Scale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Proceedings",
}