229 research outputs found
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
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Spectral non-local restoration of hyperspectral images with low-rank property
Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI
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
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique
widely used in hyperspectral images (HSIs) denoising or completion. However,
the uncertainty quantification of the restored HSI has not been addressed to
date. Accurate uncertainty quantification of the denoised HSI facilitates to
applications such as multi-source or multi-scale data fusion, data
assimilation, and product uncertainty quantification, since these applications
require an accurate approach to describe the statistical distributions of the
input data. Therefore, we propose a prior-free closed-form element-wise
uncertainty quantification method for LRMA-based HSI restoration. Our
closed-form algorithm overcomes the difficulty of the HSI patch mixing problem
caused by the sliding-window strategy used in the conventional LRMA process.
The proposed approach only requires the uncertainty of the observed HSI and
provides the uncertainty result relatively rapidly and with similar
computational complexity as the LRMA technique. We conduct extensive
experiments to validate the estimation accuracy of the proposed closed-form
uncertainty approach. The method is robust to at least 10% random impulse noise
at the cost of 10-20% of additional processing time compared to the LRMA. The
experiments indicate that the proposed closed-form uncertainty quantification
method is more applicable to real-world applications than the baseline Monte
Carlo test, which is computationally expensive. The code is available in the
attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and
Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS
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