179 research outputs found
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
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained
great popularity for their remarkable performance but often demand expensive
network retraining whenever the specifics of task changes. In this paper, we
propose to restore HSIs in a unified approach with an effective plug-and-play
method, which can jointly retain the flexibility of optimization-based methods
and utilize the powerful representation capability of deep neural networks.
Specifically, we first develop a new deep HSI denoiser leveraging gated
recurrent convolution units, short- and long-term skip connections, and an
augmented noise level map to better exploit the abundant spatio-spectral
information within HSIs. It, therefore, leads to the state-of-the-art
performance on HSI denoising under both Gaussian and complex noise settings.
Then, the proposed denoiser is inserted into the plug-and-play framework as a
powerful implicit HSI prior to tackle various HSI restoration tasks. Through
extensive experiments on HSI super-resolution, compressed sensing, and
inpainting, we demonstrate that our approach often achieves superior
performance, which is competitive with or even better than the state-of-the-art
on each task, via a single model without any task-specific training.Comment: code at https://github.com/Zeqiang-Lai/DPHSI
A Tensor-Based Dictionary Learning Approach to Tomographic Image Reconstruction
We consider tomographic reconstruction using priors in the form of a
dictionary learned from training images. The reconstruction has two stages:
first we construct a tensor dictionary prior from our training data, and then
we pose the reconstruction problem in terms of recovering the expansion
coefficients in that dictionary. Our approach differs from past approaches in
that a) we use a third-order tensor representation for our images and b) we
recast the reconstruction problem using the tensor formulation. The dictionary
learning problem is presented as a non-negative tensor factorization problem
with sparsity constraints. The reconstruction problem is formulated in a convex
optimization framework by looking for a solution with a sparse representation
in the tensor dictionary. Numerical results show that our tensor formulation
leads to very sparse representations of both the training images and the
reconstructions due to the ability of representing repeated features compactly
in the dictionary.Comment: 29 page
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Tensor-based methods have recently emerged as a more natural and effective
formulation to address many problems in hyperspectral imaging. In hyperspectral
unmixing (HU), low-rank constraints on the abundance maps have been shown to
act as a regularization which adequately accounts for the multidimensional
structure of the underlying signal. However, imposing a strict low-rank
constraint for the abundance maps does not seem to be adequate, as important
information that may be required to represent fine scale abundance behavior may
be discarded. This paper introduces a new low-rank tensor regularization that
adequately captures the low-rank structure underlying the abundance maps
without hindering the flexibility of the solution. Simulation results with
synthetic and real data show that the the extra flexibility introduced by the
proposed regularization significantly improves the unmixing results
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
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
This paper proposes a non-data-driven deep neural network for spectral image
recovery problems such as denoising, single hyperspectral image
super-resolution, and compressive spectral imaging reconstruction. Unlike
previous methods, the proposed approach, dubbed Mixture-Net, implicitly learns
the prior information through the network. Mixture-Net consists of a deep
generative model whose layers are inspired by the linear and non-linear
low-rank mixture models, where the recovered image is composed of a weighted
sum between the linear and non-linear decomposition. Mixture-Net also provides
a low-rank decomposition interpreted as the spectral image abundances and
endmembers, helpful in achieving remote sensing tasks without running
additional routines. The experiments show the MixtureNet effectiveness
outperforming state-of-the-art methods in recovery quality with the advantage
of architecture interpretability
- âŠ