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» Sparse Image Reconstruction using Sparse Priors
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161
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TIP
1998
142views more  TIP 1998»
15 years 3 months ago
Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF
Abstract—We propose a method for the reconstruction of signals and images observed partially through a linear operator with a large support (e.g., a Fourier transform on a sparse...
Mila Nikolova, Jérôme Idier, Ali Moha...
134
Voted
ICML
2010
IEEE
15 years 4 months ago
Learning Fast Approximations of Sparse Coding
In Sparse Coding (SC), input vectors are reconstructed using a sparse linear combination of basis vectors. SC has become a popular method for extracting features from data. For a ...
Karol Gregor, Yann LeCun
133
Voted
ICIP
2006
IEEE
16 years 5 months ago
Robust Diffusion of Structural Flows for Volumetric Image Interpolation
In this paper we propose a set of algorithms that combine the anisotropic smoothing using the heat kernel with the outlier rejection capability of robust statistics. The proposed ...
Ashish Doshi, Adrian G. Bors
148
Voted
TSP
2010
14 years 10 months ago
Variance-component based sparse signal reconstruction and model selection
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
Kun Qiu, Aleksandar Dogandzic
133
Voted
SMI
2006
IEEE
134views Image Analysis» more  SMI 2006»
15 years 9 months ago
Evolution of T-Spline Level Sets with Distance Field Constraints for Geometry Reconstruction and Image Segmentation
We study the evolution of T-spline level sets (i.e, implicitly defined T-spline curves and surfaces). The use of T-splines leads to a sparse representation of the geometry and al...
Huaiping Yang, Matthias Fuchs, Bert Jüttler, ...