89 research outputs found
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
This paper proposes a general destriping framework using flatness
constraints, where we can handle various regularization functions in a unified
manner. Removing stripe noise, i.e., destriping, from remote sensing images is
an essential task in terms of visual quality and subsequent processing. Most of
the existing methods are designed by combining a particular image
regularization with a stripe noise characterization that cooperates with the
regularization, which precludes us to examine different regularizations to
adapt to various target images. To resolve this, we formulate the destriping
problem as a convex optimization problem involving a general form of image
regularization and the flatness constraints, a newly introduced stripe noise
characterization. This strong characterization enables us to consistently
capture the nature of stripe noise, regardless of the choice of image
regularization. For solving the optimization problem, we also develop an
efficient algorithm based on a diagonally preconditioned primal-dual splitting
algorithm (DP-PDS), which can automatically adjust the stepsizes. The
effectiveness of our framework is demonstrated through destriping experiments,
where we comprehensively compare combinations of image regularizations and
stripe noise characterizations using hyperspectral images (HSI) and infrared
(IR) videos.Comment: submitted to IEEE Transactions on Geoscience and Remote Sensin
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
Hyperspectral images (HSIs) are inevitably degraded by a mixture of various
types of noise, such as Gaussian noise, impulse noise, stripe noise, and dead
pixels, which greatly limits the subsequent applications. Although various
denoising methods have already been developed, accurately recovering the
spatial-spectral structure of HSIs remains a challenging problem to be
addressed. Furthermore, serious stripe noise, which is common in real HSIs, is
still not fully separated by the previous models. In this paper, we propose an
adaptive hyperLaplacian regularized low-rank tensor decomposition (LRTDAHL)
method for HSI denoising and destriping. On the one hand, the stripe noise is
separately modeled by the tensor decomposition, which can effectively encode
the spatial-spectral correlation of the stripe noise. On the other hand,
adaptive hyper-Laplacian spatial-spectral regularization is introduced to
represent the distribution structure of different HSI gradient data by
adaptively estimating the optimal hyper-Laplacian parameter, which can reduce
the spatial information loss and over-smoothing caused by the previous total
variation regularization. The proposed model is solved using the alternating
direction method of multipliers (ADMM) algorithm. Extensive simulation and
real-data experiments all demonstrate the effectiveness and superiority of the
proposed method
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
Robust Constrained Hyperspectral Unmixing Using Reconstructed-Image Regularization
Hyperspectral (HS) unmixing is the process of decomposing an HS image into
material-specific spectra (endmembers) and their spatial distributions
(abundance maps). Existing unmixing methods have two limitations with respect
to noise robustness. First, if the input HS image is highly noisy, even if the
balance between sparse and piecewise-smooth regularizations for abundance maps
is carefully adjusted, noise may remain in the estimated abundance maps or
undesirable artifacts may appear. Second, existing methods do not explicitly
account for the effects of stripe noise, which is common in HS measurements, in
their formulations, resulting in significant degradation of unmixing
performance when such noise is present in the input HS image. To overcome these
limitations, we propose a new robust hyperspectral unmixing method based on
constrained convex optimization. Our method employs, in addition to the two
regularizations for the abundance maps, regularizations for the HS image
reconstructed by mixing the estimated abundance maps and endmembers. This
strategy makes the unmixing process much more robust in highly-noisy scenarios,
under the assumption that the abundance maps used to reconstruct the HS image
with desirable spatio-spectral structure are also expected to have desirable
properties. Furthermore, our method is designed to accommodate a wider variety
of noise including stripe noise. To solve the formulated optimization problem,
we develop an efficient algorithm based on a preconditioned primal-dual
splitting method, which can automatically determine appropriate stepsizes based
on the problem structure. Experiments on synthetic and real HS images
demonstrate the advantages of our method over existing methods.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensin
An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal
The stripe noise existing in remote sensing images badly degrades the visual
quality and restricts the precision of data analysis. Therefore, many
destriping models have been proposed in recent years. In contrast to these
existing models, in this paper, we propose a nonconvex model with a DC function
(i.e., the difference of convex functions) structure to remove the strip noise.
To solve this model, we make use of the DC structure and apply an inexact
proximal majorization-minimization algorithm with each inner subproblem solved
by the alternating direction method of multipliers. It deserves mentioning that
we design an implementable stopping criterion for the inner subproblem, while
the convergence can still be guaranteed. Numerical experiments demonstrate the
superiority of the proposed model and algorithm.Comment: 19 pages, 3 figure
Column-Spatial Correction Network for Remote Sensing Image Destriping
The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments
Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”
publishedVersionNon peer reviewe
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
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