2,501 research outputs found
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Designing Gabor windows using convex optimization
Redundant Gabor frames admit an infinite number of dual frames, yet only the
canonical dual Gabor system, constructed from the minimal l2-norm dual window,
is widely used. This window function however, might lack desirable properties,
e.g. good time-frequency concentration, small support or smoothness. We employ
convex optimization methods to design dual windows satisfying the Wexler-Raz
equations and optimizing various constraints. Numerical experiments suggest
that alternate dual windows with considerably improved features can be found
Tomographic inversion using -norm regularization of wavelet coefficients
We propose the use of regularization in a wavelet basis for the
solution of linearized seismic tomography problems , allowing for the
possibility of sharp discontinuities superimposed on a smoothly varying
background. An iterative method is used to find a sparse solution that
contains no more fine-scale structure than is necessary to fit the data to
within its assigned errors.Comment: 19 pages, 14 figures. Submitted to GJI July 2006. This preprint does
not use GJI style files (which gives wrong received/accepted dates).
Corrected typ
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
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