Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"Sparsity constraint"”.
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Estimation of channelized features in geological media using sparsity constraint
… linear observations in an incoherent basis. The sparsity constraint is applied in the DCT domain and reconstruction of unknown DCT coefficients is carried out through incorporation of point measurements and prior knowledge in the spatial domain. The approach appears to be generally applicable for …
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Deconvolution and sparsity based image restoration
… an explicit blur estimation and strict positive constraint on the observed and original image, are utilized to retrieve the latent original image. The third method is derived using successive minimization of KLD between a model and a desired family of probability distributions. This algorithm can …
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Deconvolution and sparsity based image restoration
… an explicit blur estimation and strict positive constraint on the observed and original image, are utilized to retrieve the latent original image. The third method is derived using successive minimization of KLD between a model and a desired family of probability distributions. This algorithm can …
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Dynamic speech imaging with low-rank approximation
… capture speech dynamics. The spatial-spectral sparsity constraint is further incorporated into the basic PS model-based reconstruction to improve reconstruction quality. The effectiveness of the above approaches is demonstrated through systematic simulations and preliminary in vivo experiments.
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Towards a dynamic view of genetic networks: A Kalman filtering framework for recovering temporally-rewiring stable networks from undersampled data
… the network connectivity at each time point. The sparsity constraint is enforced using the weighted l1-norm; and the stability constraint is incorporated using the Lyapounov stability condition. The proposed constrained Kalman lter is formulated to preserve the convex nature of the problem. The …
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Scanning-free compressive reconstruction of object motion with sub-pixel accuracy
… operator to the hologram and applied a sparsity constraint on the object derivative space for compressive holography. Together with spectrum domain zero-padding, our compressive algorithm allows for sub-pixel accuracy edge localization. The extension to the 2D case is not trivial. It has …
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Source localization via time difference of arrival
… a TDOA estimation method that exploits the sparsity of multipath channels is proposed. This is formulated as an f1-regularization problem, where the f1-norm is used as channel sparsity constraint. For the second stage, three methods are proposed to offer high accuracy at different …
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Through-the-Wall Imaging and Multipath Exploitation
… accuracy. In a related effort, we utilize the sparsity constraint to improve electromagnetic imaging of hidden conducting targets, assuming that a set of equivalent sources can be substituted for the targets. We derive a linear measurement model and employ <italic>l</italic><sub>1</sub> …
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Fast MRI with sparse sampling: models, algorithms, and applications
… advantages of incorporating both low-rank and sparsity models into the proposed formulation with a real-time cardiac imaging application. Second, we extend the joint low-rank and sparsity model to accelerate an important class of quantitative MRI problems, i.e., MR parameter mapping. We …
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High-resolution full-vocal-tract dynamic speech magnetic resonance imaging
… is enabled by introducing a deformation-based sparsity constraint that not only improves image reconstruction quality but also analyzes articulatory motion by a high-resolution deformation field; and (d) accurate assessment of subject-specific motion as opposed to generic motion pattern is …
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Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging
Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important step towards quantitative studies. Although a large body of work has been done on spectral estimation over the past …