QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filtering

TL;DR

This paper introduces QSpeckleFilter, a novel quantum machine learning model designed to improve SAR speckle filtering, demonstrating superior performance over previous methods in key image quality metrics and advancing Earth Observation capabilities.

Contribution

The paper presents a new QML-based speckle filtering method that leverages quantum algorithms, offering improved accuracy and computational efficiency over prior approaches.

Findings

QSpeckleFilter outperforms previous models in PSNR and SSIM.

Quantum algorithms enhance speckle filtering accuracy.

The method advances Earth Observation applications.

Abstract

The use of Synthetic Aperture Radar (SAR) has greatly advanced our capacity for comprehensive Earth monitoring, providing detailed insights into terrestrial surface use and cover regardless of weather conditions, and at any time of day or night. However, SAR imagery quality is often compromised by speckle, a granular disturbance that poses challenges in producing accurate results without suitable data processing. In this context, the present paper explores the cutting-edge application of Quantum Machine Learning (QML) in speckle filtering, harnessing quantum algorithms to address computational complexities. We introduce here QSpeckleFilter, a novel QML model for SAR speckle filtering. The proposed method compared to a previous work from the same authors showcases its superior performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on a testing dataset, and it opens new avenues for Earth Observation (EO) applications.