1,257 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
Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections
In this paper, we propose a novel low-tubal-rank tensor recovery model, which
directly constrains the tubal rank prior for effectively removing the mixed
Gaussian and sparse noise in hyperspectral images. The constraints of
tubal-rank and sparsity can govern the solution of the denoised tensor in the
recovery procedure. To solve the constrained low-tubal-rank model, we develop
an iterative algorithm based on bilateral random projections to efficiently
solve the proposed model. The advantage of random projections is that the
approximation of the low-tubal-rank tensor can be obtained quite accurately in
an inexpensive manner. Experimental examples for hyperspectral image denoising
are presented to demonstrate the effectiveness and efficiency of the proposed
method.Comment: Accepted by IGARSS 201
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
In remote sensing images, the presence of thick cloud accompanying cloud
shadow is a high probability event, which can affect the quality of subsequent
processing and limit the scenarios of application. Hence, removing the thick
cloud and cloud shadow as well as recovering the cloud-contaminated pixels is
indispensable to make good use of remote sensing images. In this paper, a novel
thick cloud removal method for remote sensing images based on temporal
smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
The basic idea of TSSTO is that the thick cloud and cloud shadow are not only
sparse but also smooth along the horizontal and vertical direction in images
while the clean images are smooth along the temporal direction between images.
Therefore, the sparsity norm is used to boost the sparsity of the cloud and
cloud shadow, and unidirectional total variation (UTV) regularizers are applied
to ensure the unidirectional smoothness. This paper utilizes alternation
direction method of multipliers to solve the presented model and generate the
cloud and cloud shadow element as well as the clean element. The cloud and
cloud shadow element is purified to get the cloud area and cloud shadow area.
Then, the clean area of the original cloud-contaminated images is replaced to
the corresponding area of the clean element. Finally, the reference image is
selected to reconstruct details of the cloud area and cloud shadow area using
the information cloning method. A series of experiments are conducted both on
simulated and real cloud-contaminated images from different sensors and with
different resolutions, and the results demonstrate the potential of the
proposed TSSTO method for removing cloud and cloud shadow from both qualitative
and quantitative viewpoints
Non-local tensor completion for multitemporal remotely sensed images inpainting
Remotely sensed images may contain some missing areas because of poor weather
conditions and sensor failure. Information of those areas may play an important
role in the interpretation of multitemporal remotely sensed data. The paper
aims at reconstructing the missing information by a non-local low-rank tensor
completion method (NL-LRTC). First, nonlocal correlations in the spatial domain
are taken into account by searching and grouping similar image patches in a
large search window. Then low-rankness of the identified 4-order tensor groups
is promoted to consider their correlations in spatial, spectral, and temporal
domains, while reconstructing the underlying patterns. Experimental results on
simulated and real data demonstrate that the proposed method is effective both
qualitatively and quantitatively. In addition, the proposed method is
computationally efficient compared to other patch based methods such as the
recent proposed PM-MTGSR method
- …