# Nonlinear Spectral Image Fusion

## TL;DR

This paper introduces a nonlinear spectral TV decomposition framework for image fusion that preserves edges and local features, enabling effective transfer and manipulation of image details.

## Contribution

The paper presents a novel spectral TV decomposition method for image fusion, outperforming traditional techniques in preserving edges and local features.

## Key findings

- Effective transfer of features like wrinkles in face images.
- Outperforms Poisson editing, osmosis, wavelet, and Laplacian pyramid fusion.
- Suitable for semi- and fully-automatic image editing.

## 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.