Nonlinear Spectral Image Fusion

• Published in: Scale Space and Variational Methods in Computer Vision Vol. 10302; pp. 41 - 53
• Main Authors: Benning, Martin, Möller, Michael, Nossek, Raz Z., Burger, Martin, Cremers, Daniel, Gilboa, Guy, Schönlieb, Carola-Bibiane
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Image composition; Image fusion; Multiscale methods; Nonlinear spectral decomposition; Total variation regularization
• ISBN: 3319587706; 9783319587707
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-58771-4_4

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.