Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Poddar, Sunrita"'
We introduce a continuous domain framework for the recovery of a planar curve from a few samples. We model the curve as the zero level set of a trigonometric polynomial. We show that the exponential feature maps of the points on the curve lie on a lo
Externí odkaz:
http://arxiv.org/abs/1810.11575
Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable u
Externí odkaz:
http://arxiv.org/abs/1807.03845
Autor:
Poddar, Sunrita, Mohsin, Yasir, Ansah, Deidra, Thattaliyath, Bijoy, Ashwath, Ravi, Jacob, Mathews
We introduce a novel bandlimited manifold framework and an algorithm to recover freebreathing and ungated cardiac MR images from highly undersampled measurements. The image frames in the free breathing and ungated dataset are assumed to be points on
Externí odkaz:
http://arxiv.org/abs/1802.08909
Autor:
Poddar, Sunrita, Jacob, Mathews
The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering algor
Externí odkaz:
http://arxiv.org/abs/1801.01455
Autor:
Poddar, Sunrita, Jacob, Mathews
We introduce a framework for the recovery of points on a smooth surface in high-dimensional space, with application to dynamic imaging. We assume the surface to be the zero-level set of a bandlimited function. We show that the exponential maps of the
Externí odkaz:
http://arxiv.org/abs/1801.00886
Autor:
Poddar, Sunrita, Jacob, Mathews
We introduce a continuous domain framework for the recovery of points on a surface in high dimensional space, represented as the zero-level set of a bandlimited function. We show that the exponential maps of the points on the surface satisfy annihila
Externí odkaz:
http://arxiv.org/abs/1801.00890
Autor:
Poddar, Sunrita, Jacob, Mathews
The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in
Externí odkaz:
http://arxiv.org/abs/1709.01870
We introduce a two step algorithm with theoretical guarantees to recover a jointly sparse and low-rank matrix from undersampled measurements of its columns. The algorithm first estimates the row subspace of the matrix using a set of common measuremen
Externí odkaz:
http://arxiv.org/abs/1412.2669
We consider the recovery of a low rank and jointly sparse matrix from under sampled measurements of its columns. This problem is highly relevant in the recovery of dynamic MRI data with high spatio-temporal resolution, where each column of the matrix
Externí odkaz:
http://arxiv.org/abs/1412.2700
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