Nonlocal CNN SAR Image Despeckling


We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.


D. Cozzolino and L. Verdoliva and G. Scarpa and G. Poggi
Nonlocal CNN SAR Image Despeckling
Remote Sensing, vol. 12, no. 6, 2020.


Python Code on GitHub:


author={D. Cozzolino and L. Verdoliva and G. Scarpa and G. Poggi},
journal={Remote Sensing}, title={Nonlocal CNN SAR Image Despeckling},
doi={10.3390/rs12061006} }