2,097 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
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
Unsupervised spectral sub-feature learning for hyperspectral image classification
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods
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
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