Nonlinear Spectral Image Fusion
This provides a tool for semi- and fully-automatic image editing and fusion, but it is incremental as it builds on existing spectral TV methods.
The paper tackled the problem of image fusion and manipulation by applying nonlinear spectral decompositions based on total variation regularization, demonstrating its effectiveness in transferring features like wrinkles between images through numerical experiments.
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.