A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model

We are motivated by a recently developed nonlinear inverse scale space method for image denoising [5, 6], whereby noise can be removed with minimal degradation. The additive noise model has been studied extensively, using the ROF model [23], an iterative regularization method [21], and the inverse scale space flow [5, 6]. However, the multiplicative noise model has not been studied thoroughly yet. Earlier total variation models for the multiplicative noise cannot easily be extended to the inverse scale space, due to the lack of global convexity. In this paper, we review existing multiplicative models and present a new total variation framework for the multiplicative noise model, which is globally strictly convex. We extend this convex model to the nonlinear inverse scale space flow, and its corresponding relaxed inverse scale space flow. We demonstrate the convergence of the flow for the multiplicative noise model, as well as its regularization effect and its relation to the Bregman di...

• Type: Journal
• Year: 2008
• Where: SIAMIS
• Authors: Jianing Shi, Stanley Osher
• Keywords: Inverse Scale Space, Multiplicative Noise, Multiplicative Noise Model, SIAMIS 2008