Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
This addresses the problem of removing speckle noise from SAR images for remote sensing applications, offering an incremental improvement by eliminating the need for synthetic training data.
The paper tackles SAR despeckling by proposing a self-supervised Bayesian method using blind-spot convolutional neural networks, which trains on noisy images without clean ground truth, achieving performance close to supervised methods on synthetic data and competitive results on real data.
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.