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-based"”.
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Deconvolution and sparsity based image restoration
… iterative least square and maximum likelihood based deconvolution methods are derived for image deblurring application. Three novel methods are presented i) a hybrid Fourier-wavelet deblurring (HFW) method based on expectation maximization (EM) approach, ii) sparse non-negative matrix …
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Deconvolution and sparsity based image restoration
… iterative least square and maximum likelihood based deconvolution methods are derived for image deblurring application. Three novel methods are presented i) a hybrid Fourier-wavelet deblurring (HFW) method based on expectation maximization (EM) approach, ii) sparse non-negative matrix …
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Strategies for Sparsity-based Time-Frequency Analyses
… In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments. In practice, missing data samples frequently occur during signal reception due to various reasons, e.g., propagation fading, measurement obstruction, removal of impulsive …
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Sparsity based methods for target localization in multi-sensor radar
In this dissertation, several sparsity-based methods for ground moving target indicator (GMTI) radar with multiple-input multiple-output (MIMO) random arrays are proposed. MIMO random arrays are large arrays that employ multiple transmitters and receivers, the positions of the transmitters and the …
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A Joint Dictionary-Based Single-Image Super-Resolution Model
… novel details. In recent years, leaning-based single-image super-resolution has been developed and proved to produce satisfactory results. With one or some dictionaries trained from a training set, learning-based super-resolution is able to establish a mapping relationship between …
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Algorithms for Reconstruction of hidden 3D shapes using diffused reflections
… from tomography. We aim at developing tomography based approaches and sparsity based methods to recover 3D shapes of objects "around the corner". We analyze multi-bounce propagation of light in an unknown hidden volume and demonstrate that the reflected light contains sufficient information to …
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Face recognition under varying illumination, pose and contiguous occlusion
… illumination changes and occlusion. While such sparsity-based algorithms achieve their best performance on occlusions that are not spatially correlated (i.e. random pixel corruption), we show that they can be significantly improved by harnessing prior knowledge about the pixel error …
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Imaging through scattering
… time-resolved measurements with a sparse-based optimization framework. This novel method has applications in remote sensing and in-vivo fluorescence lifetime imaging. Another method is demonstrated to resolve blood flow speed within skin tissue. This method is based on a computational …
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Potato genomics three ways: quantification of endoreduplication in tubers, a romp through the transposon terrain, and elucidation of flower color regulation
… gene expression. We then combined homology and sparsity based approaches to predict recent MITE activity, identifying five families as especially active. Finally, we expose the gene underlying the potato flower color locus, a homolog of AN2, while showing the effects it exerts on the flavonoid …
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Integration of Model- and Learning-based Methods in Image Restoration
… to combine the practical advantages of learning-based methods with the theoretical understanding that comes from model-based approaches. In fact, while deep learning methods often provide state-of-the-art performance, they usually have no performance guarantees and one cannot predict how well …
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Inference and uncertainty quantification for unsupervised structural monitoring problems
… signal processing method by combining the sparsity based regularization with the singularity expansion method. This method can provide a sparse representation of signals in complex-frequency plane and hence, more robust system identification schemes. For uncertainty quantification and …
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On the spatial predictability of wireless channels and robust networked cooperation in mobile sensor networks
… spatial predictability of a wireless channel, based on only a few measurements, become considerably important. The first contribution of this thesis is to propose a framework for predicting the spatial variations of wireless channels and to fundamentally understand wireless channel …