Bayesian Despeckling of Structured Sources

TL;DR

This paper introduces a theoretically grounded despeckling method for structured stationary stochastic sources, demonstrating improved performance on piecewise constant sources and establishing a performance lower bound.

Contribution

It presents a novel despeckling approach applicable to general structured sources, with theoretical analysis and performance bounds, advancing beyond prior heuristic methods.

Findings

Effective despeckling on piecewise constant sources

Theoretical lower bound on despeckling performance

Improved reconstruction without simplifying the signal model

Abstract

Speckle noise is a fundamental challenge in coherent imaging systems, significantly degrading image quality. Over the past decades, numerous despeckling algorithms have been developed for applications such as Synthetic Aperture Radar (SAR) and digital holography. In this paper, we aim to establish a theoretically grounded approach to despeckling. We propose a method applicable to general structured stationary stochastic sources. We demonstrate the effectiveness of the proposed method on piecewise constant sources. Additionally, we theoretically derive a lower bound on the despeckling performance for such sources. The proposed depseckler applied to the 1-Markov structured sources achieves better reconstruction performance with no strong simplification of the ground truth signal model or speckle noise.