Denoising based on wavelets and deblurring via self-organizing map for Synthetic Aperture Radar images

TL;DR

This paper introduces an unsupervised two-step image deblurring method for SAR images, combining wavelet-based noise reduction and self-organizing map learning to effectively remove blur and noise.

Contribution

It presents a novel two-step deblurring approach that integrates wavelet denoising with SOM-based deblurring, specifically tailored for SAR images with noise.

Findings

Effective noise reduction in wavelet domain

Successful application to real SAR images

Demonstrated improvement in deblurring quality

Abstract

This work deals with unsupervised image deblurring. We present a new deblurring procedure on images provided by low-resolution synthetic aperture radar (SAR) or simply by multimedia in presence of multiplicative (speckle) or additive noise, respectively. The method we propose is defined as a two-step process. First, we use an original technique for noise reduction in wavelet domain. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the denoised image to take out it the blur. This technique has been successfully applied to real SAR images, and the simulation results are presented to demonstrate the effectiveness of the proposed algorithms.