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The impact of cannabidiol consumption on oral microbiome composition
Richweissová, Viktória ; Bartoň, Vojtěch (referee) ; Čejková, Darina (advisor)
The composition of oral microbiome plays a significant role in maintaining both oral, and overall systemic health. When in equilibrium, the oral microbiome maintains the oral cavity in health. However, certain ecological shifts in the microbiota allow pathogens to manifest and cause various oral and systemic diseases. The analysis of the oral microbiome makes it possible to define the role of its components in health and disease, and the effect of various therapeutic techniques on its composition. This bachelor's thesis investigates the possible effect of toothpaste with cannabidiol on oral microbiome. The aim of this work is the bioinformatics analysis of oral microbiome communities before and after using CBD toothpaste using pipeline QIIME 2.
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Odstranění rozmazání pomocí dvou snímků s různou délkou expozice
Sabo, Jozef ; Šroubek, Filip (advisor) ; Horáček, Jan (referee)
In the presented work we study methods of image deblurring using two images of the same scene with different exposure times, focusing on two main approach categories, the so called deconvolution and non-deconvolution methods. We present theoretical backgrounds on both categories and evaluate their limitations and advantages. We dedicate one section to a comparison of both method categories on test data (images) for which we use a MATLAB implementation of the methods. We also compare the effectiveness of said methods against the results of a selected single- image de-noising algorithm. We do not focus at computational efficiency of algorithms and work with grayscale images only.
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Odstranění rozmazání pomocí dvou snímků s různou délkou expozice
Sabo, Jozef ; Šroubek, Filip (advisor) ; Horáček, Jan (referee)
In the presented work we study the methods of image deblurring using two images of the same scene with different exposure times, focusing on two main approach categories, so called deconvolution and non-deconvolution methods. We present theoretical backgrounds on both categories and evaluate their limitations and advantages. We dedicate one section to compare both method categories on test data (images) for which we our MATLAB implementation of the methods. We also compare the effectiveness of said methods against the results of a selected single-image de-noising algorithm. We do not focus at computational efficiency of algorithms and work with single-channel images only.
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Removing noise in images using deep learning methods
Strejček, Jakub ; Jakubíček, Roman (referee) ; Vičar, Tomáš (advisor)
This thesis focuses on comparing methods of denoising by deep learning and their implementation. In the last few years, it has become clear that it is not necessary to have paired data, as for noisy and clean pictures, to train convolution neural networks but it is sufficient to have only noisy pictures for denoising in particular cases. By using methods described in this thesis it is possible to effectively remove i.e. additive Gaussian noise and what more, it is possible to achieve better results than by using statistic methods, which are being used for denoising these days.
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Regularization techniques based on the least squares method
Kubínová, Marie ; Hnětynková, Iveta (advisor)
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiagonalization vector from b. This method is tested for different types of noise. Further, Hnětynková, Plešinger, and Strakoš provided an estimator of the noise level in the data. We propose a modification of this estimator based on the knowledge of the point of noise revealing. Keywords: ill-posed problems, regularization, Golub-Kahan iterative bidiagonalization, noise revealing, noise estimate, denoising 1
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Sensor Security - Verification of Image Authenticity
Juráček, Ivo ; Španěl, Michal (referee) ; Zemčík, Pavel (advisor)
Diploma thesis is about image sensor security. Goal of the thesis was study data integrity gained from the image sensors. Proposed method is about source camera identification from noise characteristics in image sensors. Research was about influence of denoising algorithms applied to digital images, which was acquired from 15 different image sensors. Finally the statistical evaluation had been done from computed results.
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Denoise Pre-Training For Segmentation Neural Networks
Kolarik, Martin
This paper proposes a method for pre-training segmentation neural networks on small datasets using unlabelled training data with added noise. The pre-training process helps the network with initial better weights settings for the training itself and also augments the training dataset when dealing with small labelled datasets especially in medical imaging. The experiment comparing results of pre-trained and not pre-trained networks on MRI brain segmentation task has shown that the denoise pre-training helps the network with faster training convergence without overfitting and achieving better results in all compared metrics even on very small datasets.
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Denoising of experimental time series
Chára, Zdeněk ; Kysela, Bohuš
This article deals with the denoising of experimental time series. Attention is focused primarily on the time series obtained by the PIV method. The noise reduction method is tested for the PIV data obtained by measuring the velocity fields in the stirred vessel.
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Audio noise reduction using deep neural networks
Talár, Ondřej ; Galáž, Zoltán (referee) ; Harár, Pavol (advisor)
The thesis focuses on the use of deep recurrent neural network, architecture Long Short-Term Memory for robust denoising of audio signal. LSTM is currently very attractive due to its characteristics to remember previous weights, or edit them not only according to the used algorithms, but also by examining changes in neighboring cells. The work describes the selection of the initial dataset and used noise along with the creation of optimal test data. For network training, the KERAS framework for Python is selected. Candidate networks for possible solutions are explored and described, followed by several experiments to determine the true behavior of the neural network.
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Regularization techniques based on the least squares method
Kubínová, Marie ; Hnětynková, Iveta (advisor)
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiagonalization vector from b. This method is tested for different types of noise. Further, Hnětynková, Plešinger, and Strakoš provided an estimator of the noise level in the data. We propose a modification of this estimator based on the knowledge of the point of noise revealing. Keywords: ill-posed problems, regularization, Golub-Kahan iterative bidiagonalization, noise revealing, noise estimate, denoising 1
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