Bayesian Despeckling of Structured Sources

2025-01-21

AI Summary

To address severe image degradation caused by speckle noise in coherent imaging, this paper proposes a Bayesian despeckling method tailored to structured stationary random sources. The method models the source using structured stochastic processes—specifically, 1-Markov processes—without imposing strong simplifying assumptions on either the underlying signal or the speckle statistics. It establishes, for the first time, a rigorous theoretical performance lower bound for structured sources, providing a verifiable benchmark for despeckling algorithms. Experimental validation on piecewise-constant sources demonstrates substantial improvement in reconstruction accuracy. The core contributions are: (1) a Bayesian estimation framework explicitly incorporating structural priors; (2) a novel derivation of the fundamental performance lower bound under structured source models; and (3) high-fidelity reconstruction achieved without restrictive modeling assumptions on signal or noise.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Structured Learning
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Security and Privacy: Data transparency and provenance

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.

Problem

• Speckle Noise
• Coherent Imaging Systems
• Image Clarity

Innovation

• Bayesian approach
• denoising technique
• coherent imaging system