255 research outputs found
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Hyperspectral imaging, providing abundant spatial and spectral information
simultaneously, has attracted a lot of interest in recent years. Unfortunately,
due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to
various degradations, such noises (random noise, HSI denoising), blurs
(Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral
and spatial downsample, HSI super-resolution). Previous HSI restoration methods
are designed for one specific task only. Besides, most of them start from the
1-D vector or 2-D matrix models and cannot fully exploit the structurally
spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this
work, we propose a unified low-rank tensor recovery model for comprehensive HSI
restoration tasks, in which non-local similarity between spectral-spatial cubic
and spectral correlation are simultaneously captured by 3-order tensors.
Further, to improve the capability and flexibility, we formulate it as a
weighted low-rank tensor recovery (WLRTR) model by treating the singular values
differently, and study its analytical solution. We also consider the exclusive
stripe noise in HSI as the gross error by extending WLRTR to robust principal
component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed
WLRTR models consistently outperform state-of-the-arts in typical low level
vision HSI tasks, including denoising, destriping, deblurring and
super-resolution.Comment: 22 pages, 22 figure
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
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various matrix norms with .
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods
Tensor completion using enhanced multiple modes low-rank prior and total variation
In this paper, we propose a novel model to recover a low-rank tensor by
simultaneously performing double nuclear norm regularized low-rank matrix
factorizations to the all-mode matricizations of the underlying tensor. An
block successive upper-bound minimization algorithm is applied to solve the
model. Subsequence convergence of our algorithm can be established, and our
algorithm converges to the coordinate-wise minimizers in some mild conditions.
Several experiments on three types of public data sets show that our algorithm
can recover a variety of low-rank tensors from significantly fewer samples than
the other testing tensor completion methods
Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution
Deep learning algorithms have demonstrated state-of-the-art performance in
various tasks of image restoration. This was made possible through the ability
of CNNs to learn from large exemplar sets. However, the latter becomes an issue
for hyperspectral image processing where datasets commonly consist of just a
few images. In this work, we propose a new approach to denoising, inpainting,
and super-resolution of hyperspectral image data using intrinsic properties of
a CNN without any training. The performance of the given algorithm is shown to
be comparable to the performance of trained networks, while its application is
not restricted by the availability of training data. This work is an extension
of original "deep prior" algorithm to HSI domain and 3D-convolutional networks.Comment: Published in ICCV 2019 Workshop
Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
In this paper, we investigate tensor recovery problems within the tensor
singular value decomposition (t-SVD) framework. We propose the partial sum of
the tubal nuclear norm (PSTNN) of a tensor. The PSTNN is a surrogate of the
tensor tubal multi-rank. We build two PSTNN-based minimization models for two
typical tensor recovery problems, i.e., the tensor completion and the tensor
principal component analysis. We give two algorithms based on the alternating
direction method of multipliers (ADMM) to solve proposed PSTNN-based tensor
recovery models. Experimental results on the synthetic data and real-world data
reveal the superior of the proposed PSTNN
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) to further improve image reconstruction. The motivating
application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion in CS applications. An
iterative algorithm based on this low-rank tensor factorization strategy,
called NLR-TFA, is presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the proposed approach,
especially at low CS sampling rates
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization
Hyperspectral image (HSI) has some advantages over natural image for various
applications due to the extra spectral information. During the acquisition, it
is often contaminated by severe noises including Gaussian noise, impulse noise,
deadlines, and stripes. The image quality degeneration would badly effect some
applications. In this paper, we present a HSI restoration method named smooth
and robust low rank tensor recovery. Specifically, we propose a structural
tensor decomposition in accordance with the linear spectral mixture model of
HSI. It decomposes a tensor into sums of outer matrix vector products, where
the vectors are orthogonal due to the independence of endmember spectrums.
Based on it, the global low rank tensor structure can be well exposited for HSI
denoising. In addition, the 3D anisotropic total variation is used for spatial
spectral piecewise smoothness of HSI. Meanwhile, the sparse noise including
impulse noise, deadlines and stripes, is detected by the l1 norm
regularization. The Frobenius norm is used for the heavy Gaussian noise in some
real world scenarios. The alternating direction method of multipliers is
adopted to solve the proposed optimization model, which simultaneously exploits
the global low rank property and the spatial spectral smoothness of the HSI.
Numerical experiments on both simulated and real data illustrate the
superiority of the proposed method in comparison with the existing ones
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Recently, single gray/RGB image super-resolution reconstruction task has been
extensively studied and made significant progress by leveraging the advanced
machine learning techniques based on deep convolutional neural networks
(DCNNs). However, there has been limited technical development focusing on
single hyperspectral image super-resolution due to the high-dimensional and
complex spectral patterns in hyperspectral image. In this paper, we make a step
forward by investigating how to adapt state-of-the-art residual learning based
single gray/RGB image super-resolution approaches for computationally efficient
single hyperspectral image super-resolution, referred as SSPSR. Specifically,
we introduce a spatial-spectral prior network (SSPN) to fully exploit the
spatial information and the correlation between the spectra of the
hyperspectral data. Considering that the hyperspectral training samples are
scarce and the spectral dimension of hyperspectral image data is very high, it
is nontrivial to train a stable and effective deep network. Therefore, a group
convolution (with shared network parameters) and progressive upsampling
framework is proposed. This will not only alleviate the difficulty in feature
extraction due to high-dimension of the hyperspectral data, but also make the
training process more stable. To exploit the spatial and spectral prior, we
design a spatial-spectral block (SSB), which consists of a spatial residual
module and a spectral attention residual module. Experimental results on some
hyperspectral images demonstrate that the proposed SSPSR method enhances the
details of the recovered high-resolution hyperspectral images, and outperforms
state-of-the-arts. The source code is available at
\url{https://github.com/junjun-jiang/SSPSRComment: Accepted for publication at IEEE Transactions on Computational
Imagin
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