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 11 of 11 for “"Denoising algorithms"”.
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Patch-based Denoising Algorithms for Single and Multi-view Images
… tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for …
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Development of novel optical sensing-based digital stethoscopes and deep learning-based heart sound denoising algorithms
… stethoscopes and deep learning-based heart sound denoising algorithms. Cardiac auscultation is the act of listening non-invasively to the sounds of the heart. It provides insights into the mechanical activity of the heart, and the sounds produced by this activity can be recorded as a …
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A Unified Framework for Image Modeling and Estimation Using Measurement Constraints
… in additive white Gaussian noise are presented. Denoising and restoration of natural images using algorithms based on these maxent priors demonstrate significant improvements in terms of both perceptual quality as well as mean-squared error over classical approaches such as adaptive Wiener …
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CellMincer: Self-Supervised Denoising of Functional Imaging
… difference in data quality. To date, few robust denoising algorithms have been designed and implemented for voltage imaging data, in part because the lack of ground truth imaging complicates the task of training such a model. This thesis introduces CellMincer, a self-supervised deep neural …
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Denoising techniques reveal neural correlates of modulation masking release in auditory cortex
… study examines the potency of three different denoising algorithms using signal detection theory. The first, amplitude rejection (AR) classifies events based on amplitude. The second, virtual referencing (VR) applies subtraction of a virtual common ground signal. The third, inter-electrode …
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Design and characterization of novel immunogens for AIDS vaccine development and evaluation of a sample inference method for NGS Illumina amplicon data
… identify the authentic differences. Many denoising algorithms have been developed, but most ignore the quality scores or compress that data. We developed ampliclust, an error modeling approach using uncompressed sequences and quality scores to infer samples in Illumina amplicon data. Our …
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Online Denoising Solutions for Forecasting Applications
… an important pre-processing step is the denoising of data before performing any action. In this research, we will propose various approaches to tackle the noisy time series in forecasting applications. For this purpose, we use different machine learning methods and information theoretical …
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Denoising by wavelet thresholding using multivariate minimum distance partial density estimation
In this thesis, we consider wavelet-based denoising of signals and images contaminated with white Gaussian noise. Existing wavelet-based denoising methods are limited because they make at least one of the following three unrealistic assumptions: (1) the wavelet coefficients are independent, (2) the …
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Learning to super-resolve images using self-similarities
… image forms the basis of self-similarity driven algorithms for image super-resolution. Self-similarity driven approaches have the appeal that they do not require any external training set; the mapping from low-resolution to high-resolution is obtained using the cross scale patch recurrence. In …
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l0 Sparse signal processing and model selection with applications
… including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many signals have a sparse representation given that the basis is suitably selected. However the difficulty lies in developing an efficient …
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Superpixel Segmentation Systems: Design and Analysis
… we discuss supervised evaluation of segmentation algorithms and the common types of error. One major part of the supervised evaluation framework is the need for ground-truth segmentations. With the growing popularity of supervised deep-learning techniques for semantic segmentation, there are large …