  SAR despeckling is a problem of paramount importance in remote sensing, since
it represents the first step of many scene analysis algorithms. Recently, deep
learning techniques have outperformed classical model-based despeckling
algorithms. However, such methods require clean ground truth images for
training, thus resorting to synthetically speckled optical images since clean
SAR images cannot be acquired. In this paper, inspired by recent works on
blind-spot denoising networks, we propose a self-supervised Bayesian
despeckling method. The proposed method is trained employing only noisy images
and can therefore learn features of real SAR images rather than synthetic data.
We show that the performance of the proposed network is very close to the
supervised training approach on synthetic data and competitive on real data.
