In this paper, classical nonlinear diffusion methods of ma- chine vision are revisited in the light of recent results in nonlinear sta- bility analysis. Global exponential convergence rates are quantified, and suggest specific choices of nonlinearities and image coupling terms. In particular, global stability and exponential convergence can be guaran- teed for nonlinear filtering of time-varying images.