  The evolution of images with physics-based dynamics is often spatially
localized and nonlinear. A switching linear dynamic system (SLDS) is a natural
model under which to pose such problems when the system's evolution randomly
switches over the observation interval. Because of the high parameter space
dimensionality, efficient and accurate recovery of the underlying state is
challenging. The work presented in this paper focuses on the common cases where
the dynamic evolution may be adequately modeled as a collection of decoupled,
locally concentrated dynamic operators. Patch-based hybrid estimators are
proposed for real-time reconstruction of images from noisy measurements given
perfect or partial information about the underlying system dynamics. Numerical
results demonstrate the effectiveness of the proposed approach for denoising in
a realistic data-driven simulation of remotely sensed cloud dynamics.
