View on GitHub

Nonlocal CNN SAR Image Despeckling

Davide Cozzolino 1      Luisa Verdoliva 2      Giuseppe Scarpa 1      Giovanni Poggi 2     
1 Department of Electrical Engineering and Information Technology, University Federico II of Naples, Italy
2 Department of Industrial Engineering, Università Federico II di Napoli, Italy


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



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