Results 31 to 40 of about 1,231,296 (158)

Sparse reconstruction methods in x-ray CT

, 2017
International audienceRecent progress in X-ray CT is contributing to the advent of new clinical applications. A common challenge for these applications is the need for new image reconstruction methods that meet tight constraints in radiation dose and ...
Monica Abella, Nicolas Ducros, Juan Felipe Perez Juste Abascal, Marinetto, Eugenio, Eugenio Marinetto, Abella, Monica, Abascal, Juan F P J, Ducros, Nicolas, Desco, Manuel, Manuel Desco, Peyrin, Françoise, Cyril Mory, Françoise Peyrin, Mory, Cyril, Claudia de Molina, de Molina, Claudia   +15 more
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Compressed sensing sparse reconstruction for coherent field imaging

, 2016
Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image.
Luo, Xiu-Juan(罗秀娟), Chen, Ming-Lai(陈明徕), Liu, Hui(刘辉), Zhang, Yu(张羽), Cao, Bei(曹蓓)   +4 more
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Sparse reconstruction of correlated multichannel activity [PDF]

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
Parametric methods for modeling sinusoidal signals with line spectra have been studied for decades. In general, these methods start by representing each sinusoidal component by means of two complex exponential functions, thereby doubling the number of unknown parameters.
Sem Peelman, Joachim van der Herten, Maarten De Vos, Wen-shin Lee, Sabine Van Huffel, Annie A. M. Cuyt   +5 more
openaire   +6 more sources

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction [PDF]

, 2017
Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse
Dong Zhang, Yongshun Zhang, Cunqian Feng
core   +1 more source

Sparse MRI and CT Reconstruction

, 2017
Sparse signal reconstruction is of the utmost importance for efficient medical imaging, conducting accurate screening for security and inspection, and for non-destructive testing.
Pouryazdanpanah Kermani, Ali
core   +1 more source

Positive Competitive Networks for Sparse Reconstruction

Neural Computation
Abstract We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with non-negativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a ...
Veronica Centorrino, Anand Gokhale, Alexander Davydov 0001, Giovanni Russo 0002, Francesco Bullo   +4 more
openaire   +3 more sources

Sparse-view X-ray CT reconstruction with Gamma regularization

, 2017
International audienceBy providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging.
Hu, Yining, Zhang, Junfeng, Yang, Jian, Chen, Yang, Coatrieux, Jean-Louis, Luo, Limin   +5 more
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Sparse representations in audio & music: from coding to source separation [PDF]

, 2010
—Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME, Gribonval, R, Daudet, Laurent, Plumbley, MD, Davies, M.E.; id_orcid, Daudet, L., Blumensath, T., Plumbley, Mark D., Gribonval, Rémi, Davies, Mike E., Plumbley, M.D., Blumensath, Thomas, Gribonval, R., Blumensath, T, Daudet, L, Davies, M.E.   +15 more
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On the convergence of the combination technique [PDF]

, 2013
Sparse tensor product spaces provide an efficient tool todiscretize higher dimensional operator equations. The direct Galerkin method in such ansatz spaces may employ hierarchical bases, interpolets, wavelets or multilevel frames. Besides, an alternative
Griebel, Michael, Michael Griebel, Helmut Harbrecht, Harbrecht, Helmut, Griebel, M., Harbrecht, H.   +5 more
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Microstate Sparse Reconstruction

, 2021
Microstate sparse reconstruction (MSR) is a backmapping algorithm that recovers all-atom structures from coarse-grained/reduced variables. MSR uses topological information (connectivity) of a given macromolecule and methods from optimization and inverse ...
Andrew Abi-Mansour
core   +1 more source