1,763 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
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution
Hyperspectral image has become increasingly crucial due to its abundant
spectral information. However, It has poor spatial resolution with the
limitation of the current imaging mechanism. Nowadays, many convolutional
neural networks have been proposed for the hyperspectral image super-resolution
problem. However, convolutional neural network (CNN) based methods only
consider the local information instead of the global one with the limited
kernel size of receptive field in the convolution operation. In this paper, we
design a network based on the transformer for fusing the low-resolution
hyperspectral images and high-resolution multispectral images to obtain the
high-resolution hyperspectral images. Thanks to the representing ability of the
transformer, our approach is able to explore the intrinsic relationships of
features globally. Furthermore, considering the LR-HSIs hold the main spectral
structure, the network focuses on the spatial detail estimation releasing from
the burden of reconstructing the whole data. It reduces the mapping space of
the proposed network, which enhances the final performance. Various experiments
and quality indexes show our approach's superiority compared with other
state-of-the-art methods
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