Bayesian Despeckling of Structured Sources
This addresses image quality degradation in applications like Synthetic Aperture Radar and digital holography, but appears incremental as it builds on existing despeckling algorithms.
The paper tackles the problem of speckle noise in coherent imaging systems by proposing a theoretically grounded despeckling method for general structured stationary stochastic sources, achieving better reconstruction performance for 1-Markov structured sources without strong simplifications.
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.