Variational Osmosis for Non-linear Image Fusion

Abstract : We propose a new variational model for nonlinear image fusion. Our approach incorporates the osmosis model proposed in Vogel et al. (2013) and Weickert et al. (2013) as an energy term in a variational model. The osmosis energy is known to realize visually plausible image data fusion. As a consequence, our method is invariant to multiplicative brightness changes. On the practical side, it requires minimal supervision and parameter tuning and can encode prior information on the structure of the images to be fused. We develop a primal-dual algorithm for solving this new image fusion model and we apply the resulting minimisation scheme to multi-modal image fusion for face fusion, colour transfer and some cultural heritage conservation challenges. Visual comparison to state-of-the-art proves the quality and flexibility of our method.
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Submitted on : Monday, October 21, 2019 - 3:26:03 PM
Last modification on : Monday, October 28, 2019 - 2:58:03 PM





Simone Parisotto, Luca Calatroni, Aurélie Bugeau, Nicolas Papadakis, Carola-Bibiane Schönlieb. Variational Osmosis for Non-linear Image Fusion. 2019. ⟨hal-02314972⟩



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