The aim of this thesis is implementation of wavelet transform algorithm for noise reduction. The noise reduction itself is focused on improving informative capabilities of sonographic (ultrasound) images in medicine. For this purpose, thresholding of detailed coefficients on individual levels of multiresolution analysis was used. Common procedures were not used for searching for the most suitable thresholds of those levels. The alternative concept's design is based on fundamental empirical approach, where the individual thresholds are optimised by evolution algorithms. However, with this algorithmic procedure, more problems manifest regarding the objective evaluation of the success of noise reduction. Because of this, the program uses commonly used parameters such as mean square error of the whole image, linear slope edge approximation, relative contrast of two differently bright and distinct points and the standard deviation of compact surface. Described theoretical knowledge is used in developed application DTWT. It executes multilevel decomposition and reversed reconstruction by discrete time wavelet transform, thresholding of detailed coefficients and final evaluation of performed noise reduction. The developed tool can be used separately to reduce noise. For our purposes, it has been modified in way, that it executed through the component for evolutionary optimization of parameters (Optimize Parameters) in created scenario in RapidMiner program. In the optimization process, this component used evaluation received from DTWT program as fitness function. Optimal thresholds were sought separately for three wavelet families - Daubeschies, Symmlets and Coiflets. The evolution algorithm chose soft threshold for all three wavelet families. In comparison to hard threshold, it is more suitable for noise reduction, but it has tendencies to blur the edges more. The devised method had in most cases greater evaluated success of noise reduction with wavelet transform with threshold search done by evolution algorithms, than commonly used filters. In visual comparison however the wavelet transform introduced some minor depreciating artefacts into the image. It is always about compromise between noise reduction and maximal preservation of image information. Objectively evaluating this dilemma is not easy and is always dependant on subjective viewpoint which in case of sonographic images is that of the attending physician.

The master's thesis is concerned with digital image denoising methods. The theoretical part explains some elementary terms related to image processing, image noise, categorization of noise and quality determining criteria of denoising process. There are also particular denoising methods described, mentioning their advantages and disadvantages in this paper. The practical part deals with an implementation of the selected denoising methods in a Java, in the environment of application RapidMiner. In conclusion, the results obtained by different methods are compared.

The goal of this work is to study the principles of delineation of ECG signals, wavelet transformation and transformation approaches to increase the number of available leads. Consequently, the knowledge was used to create delineation algorithm in Matlab. The algorithm was tested on complete CSE database. The obtained results were compared with the criteria which are set for the CSE database. In this work were realized six transformation approaches to increase the number of available leads. All of them were analyzed by delineation algorithm. In the work was examined, whether the transformation increase the efficiency of detection.

The thesis deals with addition of pixels outside the image. Lists some methods for inpainting using computers and highlights the pitfalls that appear here. Examines methods for interpolation and approximation of functions in order to find the best method for extrapolating the image beyond its borders. Describes the basics of Wavelet transformation and Multiresolution analysis and briefly discusses about spatial filtering, edge detection and the algorithm OMP, falling within the sparse representation of signals. Theoretical knowledge of these areas are used in the design of several methods for adding pixels outside the image. PSNR and SSIM are used to compare achieved results. Also discussed is the development environment of MATLAB as a tool for the implementation of algorithms that practically solves the given problem.

This work deals with delineation of ECG signals. First we will become familiar with ECG and commonly used processing methods. Various transformation methods of ECG leads. Further we will describe methods of delineation ECG signals. Second part is dedicated to metod used in this work for delineation of ECG signals from CSE database. Particulary method using Dyadic Wavelet Transform. Work include, description of used program and results. Third part is dedicated to methods of ECG leads transformations and cluster analysis. In conclusion we will evaluate the results.

The thesis discusses the possibilities of using contour shape classification for detection of mis-segmented bones in computed tomography (CT) data. In the first part there are presented published methods and algorithms which deal with the segmentation of bone structures in CT data. Then segmentation of cortical bones is implemented by a simple thresholding with global threshold. The threshold is determined by the optimized fitting of a selected type probability distribution to the histogram. Subsequently, the thesis describes some important shape descriptors that can quantitatively describe the shapes of objects in the image. Further, the contour extraction is implemented and a suitable shape descriptor, cumulative angular function, is applied. Finally, the points which can potentially indicate mis-segmented bones are detected by using continuous wavelet transform. The proposed technique is tested on the real CT data.

This project is aimed at sleeping EEG signal, especially at searching of sleeping graphoelements and next at processing signal, witch this segmentation go before. Charakterization of sleeping graphoelements and problems with their classification are outlined here. Principle of two detection methods of k-komplex are explained and processed by Matlab with graphically representation of results. Results of automatic classification are compared with scoring of two experts.

The main subject of this work represents the time-frequency signal analysis. Firstly, it intends to provide the most essential theoretical background with focus on the continuous wavelet transform, where also a comparison of the key features with its close relative the short-time Fourier transform is performed. Afterwards, there follows a demonstration of the purpose with a practical example. The particular aim is to create a phase vocoder solution for modifying the length of a sound record duration and pitch shifting. Here, in this place, the functional principles, design, procedure of assembling, outputs and achieved results are well documented.

The document is discussing the issue of the computer vision with ability to character recignition in the image. Wavelet transform is used for preprocessing the image. Pixel energy feature is firstly used for searchich candidate text pixels. Density region growing method is then used to collect candidate pixels to the separate regions, which will be candidate text regions. Several of the features are calculated over the regions and the SVM classifier is used to derive, if the region is really a text region or not.

We propose a recursive formula for moments of scaling function and sum rule. It is shown that some quadrature formulae has a higher degree of accuracy under proposed moment condition. On this basis we obtain higher accuracy formula for wavelet expansion coefficients which are needed to start the fast wavelet transform and estimate convergence rate of wavelet approximation and sampling of smooth functions. We also present a direct algorithm for solving refinement equation.