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

TL;DR

MuLoG is a versatile framework that integrates Gaussian denoisers into multi-channel SAR speckle reduction, enabling improved processing of complex-valued SAR images by leveraging advances in Gaussian image denoising techniques.

Contribution

The paper introduces MuLoG, a novel scheme that incorporates Gaussian denoisers into multi-channel SAR speckle reduction, addressing the unique challenges of SAR image noise.

Findings

MuLoG effectively reduces speckle in multi-channel SAR images.

The framework benefits from existing Gaussian denoisers, enhancing flexibility.

Results show method-specific artifacts that can be mitigated through comparison.

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.

Full text

MuLoG, or

How to apply Gaussian denoisersto multi-channel SAR speckle reduction?

Charles-Alban Deledalle, Loïc Denis, Sonia Tabti, Florence Tupin

See pages 1-last of mulog_emb