Image Fusiom using a joint-variational osmosis model

Abstract

The main purpose of this work is the fusion of multiple images to a single composite that offers more information than the individual input images. We focus the approach within a variational framework. First, we present the most basic variational model which is the Poisson editing and follow it up by Osmosis. Osmosis is a transport phenomenon that is omnipresent in nature. It differs f rom d iffusion by th e fa ct th at it al lows nonconstant steady states. Then we study a proposed modification t o t his model t hat i s c alled jointvariational

Osmosis that makes the overall term non-convex. The minimization of this new non-convex model gives plausible image data fusion. We minimize it using the inertial Porixmal algorithm for non convex optimization algorithm (iPiano), we apply the resulting minimization scheme to solve multi-modal face fusion, color transfer and cultural heritage conservation problems. Comparing this result with famous models visualy or quantitatively using error mesures shows the superiority and flexibility of this method. Keywords: Image fusion, Variational image fusion, Osmosis model, drfit-diffusion, non-convex optimization, gradient descent algorithms, proximal algorithms