304 research outputs found
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
Hyperspectral images (HSIs) are inevitably degraded by a mixture of various
types of noise, such as Gaussian noise, impulse noise, stripe noise, and dead
pixels, which greatly limits the subsequent applications. Although various
denoising methods have already been developed, accurately recovering the
spatial-spectral structure of HSIs remains a challenging problem to be
addressed. Furthermore, serious stripe noise, which is common in real HSIs, is
still not fully separated by the previous models. In this paper, we propose an
adaptive hyperLaplacian regularized low-rank tensor decomposition (LRTDAHL)
method for HSI denoising and destriping. On the one hand, the stripe noise is
separately modeled by the tensor decomposition, which can effectively encode
the spatial-spectral correlation of the stripe noise. On the other hand,
adaptive hyper-Laplacian spatial-spectral regularization is introduced to
represent the distribution structure of different HSI gradient data by
adaptively estimating the optimal hyper-Laplacian parameter, which can reduce
the spatial information loss and over-smoothing caused by the previous total
variation regularization. The proposed model is solved using the alternating
direction method of multipliers (ADMM) algorithm. Extensive simulation and
real-data experiments all demonstrate the effectiveness and superiority of the
proposed method
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
Revisiting Nonlocal Self-Similarity from Continuous Representation
Nonlocal self-similarity (NSS) is an important prior that has been
successfully applied in multi-dimensional data processing tasks, e.g., image
and video recovery. However, existing NSS-based methods are solely suitable for
meshgrid data such as images and videos, but are not suitable for emerging
off-meshgrid data, e.g., point cloud and climate data. In this work, we revisit
the NSS from the continuous representation perspective and propose a novel
Continuous Representation-based NonLocal method (termed as CRNL), which has two
innovative features as compared with classical nonlocal methods. First, based
on the continuous representation, our CRNL unifies the measure of
self-similarity for on-meshgrid and off-meshgrid data and thus is naturally
suitable for both of them. Second, the nonlocal continuous groups can be more
compactly and efficiently represented by the coupled low-rank function
factorization, which simultaneously exploits the similarity within each group
and across different groups, while classical nonlocal methods neglect the
similarity across groups. This elaborately designed coupled mechanism allows
our method to enjoy favorable performance over conventional NSS methods in
terms of both effectiveness and efficiency. Extensive multi-dimensional data
processing experiments on-meshgrid (e.g., image inpainting and image denoising)
and off-meshgrid (e.g., climate data prediction and point cloud recovery)
validate the versatility, effectiveness, and efficiency of our CRNL as compared
with state-of-the-art methods
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