3,166 research outputs found
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
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
Magnetic resonance (MR) image acquisition is an inherently prolonged process,
whose acceleration by obtaining multiple undersampled images simultaneously
through parallel imaging has always been the subject of research. In this
paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable
of learning multi-scale spatial-frequency features from both real and imaginary
components, for fast parallel MR image reconstruction. By reformulating the
complex operations using octave convolutions, our model shows a strong ability
to capture richer representations of MR images, while at the same time greatly
reducing the spatial redundancy. More specifically, the input feature maps and
convolutional kernels are first split into two components (i.e., real and
imaginary), which are then divided into four groups according to their spatial
frequencies. Then, our Dual-OctConv conducts intra-group information updating
and inter-group information exchange to aggregate the contextual information
across different groups. Our framework provides two appealing benefits: (i) it
encourages interactions between real and imaginary components at various
spatial frequencies to achieve richer representational capacity, and (ii) it
enlarges the receptive field by learning multiple spatial-frequency features of
both the real and imaginary components. We evaluate the performance of the
proposed model on the acceleration of multi-coil MR image reconstruction.
Extensive experiments are conducted on an {in vivo} knee dataset under
different undersampling patterns and acceleration factors. The experimental
results demonstrate the superiority of our model in accelerated parallel MR
image reconstruction. Our code is available at:
github.com/chunmeifeng/Dual-OctConv.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence
(AAAI) 202
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