Combination of geometric clustering and nonlocal means for SAR image despeckling

2014 ◽  
Vol 50 (5) ◽  
pp. 395-396 ◽  
Author(s):  
Wensen Feng ◽  
Hong Lei
Keyword(s):  
Sar Image ◽  
Nonlocal Means ◽  
Geometric Clustering ◽  
Image Despeckling

scholarly journals Nonlocal CNN SAR Image Despeckling

2020 ◽  
Vol 12 (6) ◽  
pp. 1006 ◽  
Author(s):  
Davide Cozzolino ◽  
Luisa Verdoliva ◽  
Giuseppe Scarpa ◽  
Giovanni Poggi
Keyword(s):  
Deep Learning ◽  
Weighted Average ◽  
Sar Image ◽  
Sar Images ◽  
Nonlocal Means ◽  
Analysis Window ◽  
Account Information ◽  
Measure Of Similarity ◽  
Image Despeckling ◽  
Insight Into

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.

scholarly journals deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling

2020 ◽  
pp. 1-15
Author(s):  
Adugna G. Mullissa ◽  
Diego Marcos ◽  
Devis Tuia ◽  
Martin Herold ◽  
Johannes Reiche
Keyword(s):  
Deep Learning ◽  
Sar Image ◽  
Image Despeckling
Sign in / Sign up

Export Citation Format

Share Document