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Preprints, Working Papers, ... Year : 2017

SPARSE-CODING ADAPTED TO SAR IMAGES WITH AN APPLICATION TO DESPECKLING

Abstract

In this paper, we propose a sparsity-based despeckling approach. The first main contribution of this work is the elaboration of a sparse-coding algorithm adapted to the statistics of SAR images. In fact, in most of the sparse-coding algorithms dedicated to SAR data, a logarithmic transform is applied on the data to turn the speckle modeled by a multiplicative noise into an additive noise, then, a Gaussian prior is used. However , using a more suitable prior for SAR data avoids introducing artifacts. The second main contribution proposed is to evaluate how computing a map predicting the sparsity degree of each patch could bring an improvement compared to a traditional sparse-coding approach with a low-error rate based stopping criterion.
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Dates and versions

hal-01647163 , version 1 (24-11-2017)
hal-01647163 , version 2 (06-12-2017)

Identifiers

  • HAL Id : hal-01647163 , version 2

Cite

Sonia Tabti, Luisa Verdoliva, Giovanni Poggi. SPARSE-CODING ADAPTED TO SAR IMAGES WITH AN APPLICATION TO DESPECKLING. 2017. ⟨hal-01647163v2⟩
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