205 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
Deep Learning Methods for Synthetic Aperture Radar Image Despeckling: An Overview of Trends and Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims to remove such noise so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970s, and several model-based algorithms have been developed in the years since. The field has received growing attention, sparked by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This article surveys the literature on deep learning methods applied to SAR despeckling, covering both supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods, with the objective of recognizing the most promising research lines; identify the factors that have limited the success of deep models; and propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling
We propose DoPAMINE, a new neural network based multiplicative noise
despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which
is a recently proposed neural adaptive image denoiser. While the original
N-AIDE was designed for the additive noise case, we show that the same
framework, i.e., adaptively learning a network for pixel-wise affine denoisers
by minimizing an unbiased estimate of MSE, can be applied to the multiplicative
noise case as well. Moreover, we derive a double-sided masked CNN architecture
which can control the variance of the activation values in each layer and
converge fast to high denoising performance during supervised training. In the
experimental results, we show our DoPAMINE possesses high adaptivity via
fine-tuning the network parameters based on the given noisy image and achieves
significantly better despeckling results compared to SAR-DRN, a
state-of-the-art CNN-based algorithm.Comment: AAAI 2019 Camera Ready Versio
Multi-Objective CNN Based Algorithm for SAR Despeckling
Deep learning (DL) in remote sensing has nowadays become an effective
operative tool: it is largely used in applications such as change detection,
image restoration, segmentation, detection and classification. With reference
to synthetic aperture radar (SAR) domain the application of DL techniques is
not straightforward due to non trivial interpretation of SAR images, specially
caused by the presence of speckle. Several deep learning solutions for SAR
despeckling have been proposed in the last few years. Most of these solutions
focus on the definition of different network architectures with similar cost
functions not involving SAR image properties. In this paper, a convolutional
neural network (CNN) with a multi-objective cost function taking care of
spatial and statistical properties of the SAR image is proposed. This is
achieved by the definition of a peculiar loss function obtained by the weighted
combination of three different terms. Each of this term is dedicated mainly to
one of the following SAR image characteristics: spatial details, speckle
statistical properties and strong scatterers identification. Their combination
allows to balance these effects. Moreover, a specifically designed architecture
is proposed for effectively extract distinctive features within the considered
framework. Experiments on simulated and real SAR images show the accuracy of
the proposed method compared to the State-of-Art despeckling algorithms, both
from quantitative and qualitative point of view. The importance of considering
such SAR properties in the cost function is crucial for a correct noise
rejection and details preservation in different underlined scenarios, such as
homogeneous, heterogeneous and extremely heterogeneous
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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