Kernel-Diffeomorphism Bayesian Bootstrap Filter to reduce speckle noise on SAR images

Mourad Zribi (), Ibrahim Sadok () and Bassel Marhaba ()
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Mourad Zribi: Maison de la Recherche Blaise Pascal
Ibrahim Sadok: University of Bechar
Bassel Marhaba: Al-Manar University of Tripoli

Computational Statistics, 2025, vol. 40, issue 7, No 10, 3613-3643

Abstract: Abstract Satellite imagery is frequently subject to degradation by noise during both image acquisition and transmission processes. The primary goal of noise reduction techniques is to remove Speckle noise while retaining critical features of the images. In remote sensing applications, Synthetic Aperture Radar (SAR) imagery plays a vital role. Speckle, a granular disturbance typically modelled as multiplicative noise, impacts SAR images as well as all coherent images, resulting in a reduction in image quality. Over the past three decades, numerous techniques have been proposed to mitigate Speckle noise in SAR imagery. This study proposes the Kernel-Diffeomorphism Bayesian Bootstrap Filter (KDBBF) as a novel method for satellite image restoration. The method relies on the multivariate Kernel Diffeomorphism estimator and the Bayesian Bootstrap Filter (BBF). Comparative analyses of the results produced by the new method with those of other image restoration techniques reveal superior performance in Speckle noise reduction in SAR imagery, both quantitatively and qualitatively.

Keywords: SAR image; Speckle noise; Image restoration; Kernel Diffeomorphism estimator; BBF; KDBBF (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-025-01650-1

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