title: A global approach for solving evolutive heat transfer for image denoising and inpainting.
abstract: This paper proposes an alternative to partial differential equations (PDEs) for solving problems in computer vision based on evolutive heat transfer. Traditionally, the method for solving such physics-based problems is to discretize and solve a PDE by a purely mathematical process. Instead of using the PDE, we propose to use the global heat principle and to decompose it into basic laws. We show that some of these laws admit an exact global version since they arise from conservative principles. We also show that the assumptions made about the other basic Iaws can be made wisely, taking into account knowledge about the problem and the domain. The numerical scheme is derived in a straightforward way from the modeled problem, thus providing a physical explanation for each step in the solution. The advantage of such an approach is that it minimizes the approximations made during the whole process and it modularizes it, allowing changing the application to a great number of problems. We apply the scheme to two applications: image denoising and inpainting which are modeled with heat transfer. For denoising, we propose a new approximation for the conductivity coefficient and we add thin lines to the features in order to block diffusion.
authors.affiliation: Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada. m-f.auclair@usherbrooke.ca
authors.forename: Marie-Flavie
authors.initials: MF
authors.lastname: Auclair-Fortier
authors.forename: Djemel
authors.initials: D
authors.lastname: Ziou
mesh_terms: D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D004735:Energy Transfer; D006358:Hot Temperature; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010151:Paintings; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes