Spatial-temporal switching estimators for imaging locally concentrated dynamics

TL;DR

This paper introduces patch-based hybrid estimators for real-time image reconstruction of localized, nonlinear dynamics modeled as switching linear dynamic systems, demonstrating effectiveness in denoising remotely sensed cloud data.

Contribution

It proposes a novel patch-based hybrid estimation method for efficiently recovering images with localized dynamic changes modeled as SLDS.

Findings

Effective denoising of cloud dynamics data

Real-time image reconstruction capability

Improved accuracy over traditional methods

Abstract

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.