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Open AccessArticle

Nonlocal CNN SAR Image Despeckling

1
DIETI, Università Federico II di Napoli, Via Claudio 21, 80125 Napoli, Italy
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DII, Università Federico II di Napoli, Via Claudio 21, 80125 Napoli, Italy
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Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Viale G.P. Usberti, n. 181/A – 43124 Parma (PR), Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1006; https://doi.org/10.3390/rs12061006
Received: 29 January 2020 / Revised: 5 March 2020 / Accepted: 16 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Remote Sensing Applications of Image Denoising and Restoration)
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. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); SAR despeckling; deep learning; nonlocal filtering; image restoration Synthetic Aperture Radar (SAR); SAR despeckling; deep learning; nonlocal filtering; image restoration
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MDPI and ACS Style

Cozzolino, D.; Verdoliva, L.; Scarpa, G.; Poggi, G. Nonlocal CNN SAR Image Despeckling. Remote Sens. 2020, 12, 1006.

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