MuLoG, or How to apply Gaussian denoisers to multi-channel SAR speckle reduction?

Abstract

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.

References in corpus (1)

• Postprocessing of Compressed Images via Sequential Denoising

Cited by in corpus (12)

• SAR2SAR: a semi-supervised despeckling algorithm for SAR images
• Image Restoration for Remote Sensing: Overview and Toolbox
• Multi-Objective CNN Based Algorithm for SAR Despeckling
• Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
• deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling
• SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
• Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
• Fixed-Point and Objective Convergence of Plug-and-Play Algorithms
• Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network
• Multi-temporal speckle reduction with self-supervised deep neural networks
• Gaussian Anamorphosis for Ensemble Kalman Filter Analysis of SAR-Derived Wet Surface Ratio Observations
• Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning