650 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
Multi-scale Adaptive Fusion Network for Hyperspectral Image Denoising
Removing the noise and improving the visual quality of hyperspectral images
(HSIs) is challenging in academia and industry. Great efforts have been made to
leverage local, global or spectral context information for HSI denoising.
However, existing methods still have limitations in feature interaction
exploitation among multiple scales and rich spectral structure preservation. In
view of this, we propose a novel solution to investigate the HSI denoising
using a Multi-scale Adaptive Fusion Network (MAFNet), which can learn the
complex nonlinear mapping between clean and noisy HSI. Two key components
contribute to improving the hyperspectral image denoising: A progressively
multiscale information aggregation network and a co-attention fusion module.
Specifically, we first generate a set of multiscale images and feed them into a
coarse-fusion network to exploit the contextual texture correlation.
Thereafter, a fine fusion network is followed to exchange the information
across the parallel multiscale subnetworks. Furthermore, we design a
co-attention fusion module to adaptively emphasize informative features from
different scales, and thereby enhance the discriminative learning capability
for denoising. Extensive experiments on synthetic and real HSI datasets
demonstrate that the proposed MAFNet has achieved better denoising performance
than other state-of-the-art techniques. Our codes are available at
\verb'https://github.com/summitgao/MAFNet'.Comment: IEEE JSTASRS 2023, code at: https://github.com/summitgao/MAFNe
Wavelet/shearlet hybridized neural networks for biomedical image restoration
Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
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