Sparse-coding adapted to SAR images with an application to despeckling

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, most sparse-coding algorithms for SAR data apply a logarithmic transform to data, so as to convert the noise from multiplicative to additive. Then, a Gaussian prior is adopted. However, using a more suitable prior for SAR data avoids introducing artifacts. The second main contribution proposed is to predict the optimal sparsity degree for each patch based on local image features. Experiments show that this strategy improves upon traditional sparse coding with a low-error-rate stopping criterion.