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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
Low Dose CT Image Reconstruction With Learned Sparsifying Transform
A major challenge in computed tomography (CT) is to reduce X-ray dose to a
low or even ultra-low level while maintaining the high quality of reconstructed
images. We propose a new method for CT reconstruction that combines penalized
weighted-least squares reconstruction (PWLS) with regularization based on a
sparsifying transform (PWLS-ST) learned from a dataset of numerous CT images.
We adopt an alternating algorithm to optimize the PWLS-ST cost function that
alternates between a CT image update step and a sparse coding step. We adopt a
relaxed linearized augmented Lagrangian method with ordered-subsets (relaxed
OS-LALM) to accelerate the CT image update step by reducing the number of
forward and backward projections. Numerical experiments on the XCAT phantom
show that for low dose levels, the proposed PWLS-ST method dramatically
improves the quality of reconstructed images compared to PWLS reconstruction
with a nonadaptive edge-preserving regularizer (PWLS-EP).Comment: This is a revised and corrected version of the IEEE IVMSP Workshop
paper DOI: 10.1109/IVMSPW.2016.752821
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