2,347 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
Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization
Multiplicative noise (also known as speckle noise) models are central to the
study of coherent imaging systems, such as synthetic aperture radar and sonar,
and ultrasound and laser imaging. These models introduce two additional layers
of difficulties with respect to the standard Gaussian additive noise scenario:
(1) the noise is multiplied by (rather than added to) the original image; (2)
the noise is not Gaussian, with Rayleigh and Gamma being commonly used
densities. These two features of multiplicative noise models preclude the
direct application of most state-of-the-art algorithms, which are designed for
solving unconstrained optimization problems where the objective has two terms:
a quadratic data term (log-likelihood), reflecting the additive and Gaussian
nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a
total variation or wavelet-based regularizer/prior). In this paper, we address
these difficulties by: (1) converting the multiplicative model into an additive
one by taking logarithms, as proposed by some other authors; (2) using variable
splitting to obtain an equivalent constrained problem; and (3) dealing with
this optimization problem using the augmented Lagrangian framework. A set of
experiments shows that the proposed method, which we name MIDAL (multiplicative
image denoising by augmented Lagrangian), yields state-of-the-art results both
in terms of speed and denoising performance.Comment: 11 pages, 7 figures, 2 tables. To appear in the IEEE Transactions on
Image Processing
Multi-temporal speckle reduction with self-supervised deep neural networks
Speckle filtering is generally a prerequisite to the analysis of synthetic
aperture radar (SAR) images. Tremendous progress has been achieved in the
domain of single-image despeckling. Latest techniques rely on deep neural
networks to restore the various structures and textures peculiar to SAR images.
The availability of time series of SAR images offers the possibility of
improving speckle filtering by combining different speckle realizations over
the same area. The supervised training of deep neural networks requires
ground-truth speckle-free images. Such images can only be obtained indirectly
through some form of averaging, by spatial or temporal integration, and are
imperfect. Given the potential of very high quality restoration reachable by
multi-temporal speckle filtering, the limitations of ground-truth images need
to be circumvented. We extend a recent self-supervised training strategy for
single-look complex SAR images, called MERLIN, to the case of multi-temporal
filtering. This requires modeling the sources of statistical dependencies in
the spatial and temporal dimensions as well as between the real and imaginary
components of the complex amplitudes. Quantitative analysis on datasets with
simulated speckle indicates a clear improvement of speckle reduction when
additional SAR images are included. Our method is then applied to stacks of
TerraSAR-X images and shown to outperform competing multi-temporal speckle
filtering approaches. The code of the trained models is made freely available
on the Gitlab of the IMAGES team of the LTCI Lab, T\'el\'ecom Paris Institut
Polytechnique de Paris
(https://gitlab.telecom-paris.fr/ring/multi-temporal-merlin/)
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