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 20 of 224 for “"Denoising"”.
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Image Denoising: Invertible and General Denoising Frameworks
… of downstream high-level vision tasks. Thus, denoising is a crucial preprocessing step. A fundamental challenge in image denoising is to restore recognizable frequencies in edge and fine-scaled texture regions. Traditional methods usually employ hand-crafted priors to enhance the restoration …
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Image Denoising: Invertible and General Denoising Frameworks
… of downstream high-level vision tasks. Thus, denoising is a crucial preprocessing step. A fundamental challenge in image denoising is to restore recognizable frequencies in edge and fine-scaled texture regions. Traditional methods usually employ hand-crafted priors to enhance the restoration …
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Denoising via Empirical Bayesian Pursuit
… and methods for blind signal recovery, or denoising, that employ all of the overdetermined representation coefficients. The introduction of L-unitary frames facilitates the analysis, for which many nondecimated, linear, time-frequency and time-scale representations qualify, as do mergers of …
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Partial discharge denoising for power cables
… to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in …
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Denoising and Demosaicking of Color Images
… values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white …
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Constrained imaging: denoising and sparse sampling
Made available in DSpace on 2011-05-25T14:59:20Z (GMT). No. of bitstreams: 2 Haldar_Justin.pdf: 25586752 bytes, checksum: 13b473014d3b4c607a03de95d1f71669 (MD5) license.txt: 4061 bytes, checksum: 1d9342c42cf91c0d9be87c7868170d96 (MD5)
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Imputing metabolomics with graph denoising autoencoders
The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.
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Self-supervised multi-contrast MRI denoising
… a self-supervised method for multi-contrast MRI denoising. C2S utilizes self-generated pseudo-labels from noisy data to enhance contrast fusion and Signal-to-Noise Ratio (SNR), providing a robust solution that facilitates shorter scanning times or improved spatial resolution—critical factors in …
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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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A denoising algorithm for surface EMG decomposition
… wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the …
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Rank constrained denoising in magnetic resonance imaging
… the utility of low-rank property in the denoising problem for the following two MRI modalities: diffusion magnetic resonance imaging and magnetic resonance spectroscopic imaging (MRSI). For denoising magnitude diffusion weighted image series, we utilize both low-rank and edge constraints …
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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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Deep Joint Denoising and Compression for Satellite Images
… under strict resource constraints. Instead of denoising and compressing in two separate steps, this work integrates both tasks into a single framework based on the Mean Scale Hyperprior architecture. By incorporating denoising directly in the compression process, the model dedicates fewer bits …
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Unrolling of Graph Total Variation for Image Denoising
… have enabled effective solutions in image denoising, in general their implementations overly rely on training data and require tuning of a large parameter set. In this thesis, a hybrid design that combines graph signal filtering with feature learning is proposed. It utilizes interpretable …
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Advanced numerical methods for image denoising and segmentation
Image denoising is one of the most major steps in current image processing. It is a pre-processing step which aims to remove certain unknown, random noise from an image and obtain an image free of noise for further image processing, such as image segmentation. Image segmentation, as another branch …
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Image interpolation and denoising in discrete wavelet transform domain
… is one of the most widely used methods for image denoising in the Discrete Wavelet Transform (DWT) domain. This method, however, has the drawback of blurring the edges and the textures of an image after denoising. A new algorithm is proposed in this thesis for image denoising in the DWT domain …
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