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Abstract: Deep neural networks have been used widely to learn the latent structure of datasets, across modalities such as images , shapes, and audio signals . However, existing models are generally ...
manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods: Cardiac MR image reconstruction is modeled as general compressed ...
Abstract: Deep neural networks have been used widely to learn the latent structure of datasets, across modalities such as images , shapes, and audio signals . However, existing models are generally ...
by the potential use of hidden Markov chains and fields, with observations in Riemannian manifolds , as models for complex signals and images . Hidden Markov chains and elds with observations in Riemannian manifolds ...
Abstract: Manifold models consider natural- image patches to be on a low-dimensional manifold embedded in a high dimensional state space and each patch and its similar patches to approximately lie ...
a geometric modeling framework in which the image ensemble is treated as a sampling of points from a low-dimensional manifold in the ambient signal space. Building on results that guarantee stable embeddings ...
We look at the design of projective measurements based upon image priors. If one assumes that image patches from natural imagery can be modeled as a low rank manifold , we develop an optimality ...
learning using deep neural networks been shown to be an eective tool for building sophisticated prior image models that can be applied to noise reduction in low-dose CT. A manifold is a low-dimensional ...
dimensional manifolds . Efforts to characterize the dynamics on this manifold have used piecewise linear models to describe the entire state space, but it is unknown how a single, global dynamical model can ...
Mumford–Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising ...
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