Nonlocal methods are state-of-the-art in SAR despeckling, thanks to their ability to exploit image self-similarity. Given sufficient training data, however, methods based on deep learning have proven highly competitive. Therefore, to take the best of both approaches, we investigate the use of deep learning to improve nonlocal despeckling. We use plain non-iterative nonlocal means despeckling, with weights provided, for each estimation window, by a suitably trained deep CNN. Experiments on synthetic and real SAR data prove this approach to outperform conventional nonlocal methods.
Nonlocal Sar Image Despeckling by Convolutional Neural Networks
Giuseppe Scarpa;Giovanni Poggi
2019-01-01
Abstract
Nonlocal methods are state-of-the-art in SAR despeckling, thanks to their ability to exploit image self-similarity. Given sufficient training data, however, methods based on deep learning have proven highly competitive. Therefore, to take the best of both approaches, we investigate the use of deep learning to improve nonlocal despeckling. We use plain non-iterative nonlocal means despeckling, with weights provided, for each estimation window, by a suitably trained deep CNN. Experiments on synthetic and real SAR data prove this approach to outperform conventional nonlocal methods.File in questo prodotto:
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