On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noise

TL;DR

This paper explores how Hamilton-Jacobi PDEs relate to variational image denoising models with non-additive noise, establishing new theoretical links and practical methods for solving such problems.

Contribution

It extends the connection between Hamilton-Jacobi PDEs and denoising models to non-additive noise, enabling convex optimization approaches for complex noise types.

Findings

Hamilton-Jacobi PDEs govern the solutions of non-additive noise models.

Optimal values depend on solutions to Hamilton-Jacobi PDEs.

Numerical results demonstrate effectiveness on Poisson and multiplicative noise.

Abstract

We consider image denoising problems formulated as variational problems. It is known that Hamilton-Jacobi PDEs govern the solution of such optimization problems when the noise model is additive. In this work, we address certain non-additive noise models and show that they are also related to Hamilton-Jacobi PDEs. These findings allow us to establish new connections between additive and non-additive noise imaging models. Specifically, we study how the solutions to these optimization problems depend on the parameters and the observed images. We show that the optimal values are ruled by some Hamilton-Jacobi PDEs, while the optimizers are characterized by the spatial gradient of the solution to the Hamilton-Jacobi PDEs. Moreover, we use these relations to investigate the asymptotic behavior of the variational model as the parameter goes to infinity, that is, when the influence of the noise vanishes. With these connections, some non-convex models for non-additive noise can be solved by applying convex optimization algorithms to the equivalent convex models for additive noise. Several numerical results are provided for denoising problems with Poisson noise or multiplicative noise.