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Wavelet analysis and enhancement of MR tomography and ultrasound images
Matoušek, Luděk ; Bartušek, Karel (referee) ; Smékal, Zdeněk (advisor)
Tomographic MR (Magnetic Resonance) and sonographic biosignal processing are important non-invasive diagnostic methods used in a medicine. A noise added into processed data by an amplifier of tomograph receiving part and by circuits of sonograph is resulting in a body organ diagnosis degradation. Image data are stored in a standardized DICOM medical file format. Methods using wavelet analysis for noise suppression in image data have been designed and their comparation with classical methods has been made in this work. The MATLAB was utilized for data processing and data rewriting back to the DICOM format.
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Directional Image Representations
Zátyik, Ján ; Rajmic, Pavel (referee) ; Průša, Zdeněk (advisor)
Various methods describes an image by specific shapes, which are called basis or frames. With these basis can be transformed the image into a representation by transformation coefficients. The aim is that the image can be described by a small number of coefficients to obtain so-called sparse representation. This feature can be used for example for image compression. But basis are not able to describe all the shapes that may appear in the image. This lack increases the number of transformation coefficients describing the image. The aim of this thesis is to study the general principle of calculating the transformation coefficients and to compare classical methods of image analysis with some of the new methods of image analysis. Compares effectiveness of method for image reconstruction from a limited number of coefficients and a noisy image. Also, compares image interpolation method using characteristics of two different transformations with bicubic transformation. Theoretical part describes the transformation methods. Describes some methods from aspects of multi/resolution, localization in time and frequency domains, redundancy and directionality. Furthermore, gives examples of transformations on a particular image. The practical part of the thesis compares efficiency of the Fourier, Wavelet, Contourlet, Ridgelet, Radon, Wavelet Packet and WaveAtom transform in image recontruction from a limited number of the most significant transformation coefficients. Besides, ability of image denoising using these methods with thresholding techniques applied to transformation coefficients. The last section deals with the interpolation of image interpolation by combining of two methods and compares the results with the classical bicubic interpolation.
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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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Methods of acquisition and processing of images based on sparse representations
Talár, Ondřej ; Mach, Václav (referee) ; Rajmic, Pavel (advisor)
Thesis deals with the reconstruction possibilities provided by the sparse representation of signals. This representation reduces the signal to a mere vector of elements which indicate the signal portion in the dictionary array. It outlined the problems with the quantized signal and recalled modulation type, involving a quantization and its ways. The solution is selected Douglas-Rachford algorithm that allows us to approximate on to the set of all acceptable solutions. At the end is demonstrated problem solution and several tests for presentation of created program.
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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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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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Applications of sparse data representations
Navrátilová, Barbora ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The goal of this thesis is to demonstrate practical application of sparse data representation in the processing of sparse signals. For solving several example problems - denoising, dequantization, and sparse signal decomposition - convex optimization was used. The solutions were implemented in the Matlab environment. For each of the problems, there are two solutions - one for one-dimensional, and one for two-dimensional signal.
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Polygonal Models Smoothing
Svěchovský, Radek ; Švub, Miroslav (referee) ; Kršek, Přemysl (advisor)
Object digitizing or 3D model transformation into surface representation brings defects in the form of noise. This thesis analyses the well-known approaches to the noise elimination from polygonal models. The reader will be concerned with the fundamental principles of smoothing and foremost the results of the comparison of different methods including Laplace method, algorithm Laplace-HC, Taubin's low-pass filter and bilateral filter.
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Static image enhancement using wavelet transform
Candrák, Matúš ; Rajmic, Pavel (referee) ; Smékal, Zdeněk (advisor)
In tomography and ultrasound signal processing, there is the noise build-up into the processing. Bachelor's thesis deals with static images highlighting, with denoising using wavelet transformation and edge detection with basic operators. This work describes some types of wavelts used for denoising of image and basic operators for edge detection in the image. The last part deals with a particular application for image processing, which was created in MATLAB.
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