2,699 research outputs found
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information
drawn from co-registered optical imagery. Filtering is performed by plain
patch-wise nonlocal means, operating exclusively on SAR data. However, the
filtering weights are computed by taking into account also the optical guide,
which is much cleaner than the SAR data, and hence more discriminative. To
avoid injecting optical-domain information into the filtered image, a
SAR-domain statistical test is preliminarily performed to reject right away any
risky predictor. Experiments on two SAR-optical datasets prove the proposed
method to suppress very effectively the speckle, preserving structural details,
and without introducing visible filtering artifacts. Overall, the proposed
method compares favourably with all state-of-the-art despeckling filters, and
also with our own previous optical-guided filter
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