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Wavelet Transform in Image Processing
Dostál, Martin ; Klíma, Ondřej (referee) ; Bařina, David (advisor)
The wavelet transform has been used for several decades and it is still an object of research - especially its recent modifications which are using the so-called second generation wavelets. It has several advantages over other integral transformations. The most important of them are the ability to localize both in time and frequency and an ability to decorrelate some real non-stationary signals such as images. For this reasons, the wavelet transform became an often used tool in many image processing tasks, for example in image compression, edge detection or contrast enhancement. In this thesis, the wavelet transform is explained, including the theoretical foundation and implementation for use with two-dimensional discrete signals. Some of the applications of the wavelet transform are presented and described. The wavelet transform showed to be suitable tool for edge detection, noise reduction, contrast enhancement and HDR compression.
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Wavelet Filtering of ECG Signal
Slezák, Pavel ; Vítek, Martin (referee) ; Smital, Lukáš (advisor)
The thesis deals with possibilities of using wavelet transform in applications dealing with noise reduction, primarily in the field of ECG signals denoising. We assess the impact of the various filtration parameters setting as the thresholding wavelet coefficients method, thresholds level setting and the selection of decomposition and reconstruction filter banks.. Our results are compared with the results of linear filtering. The results of wavelet Wieners filtration with pilot estimation are described below. Mainly, we tested a combination of decomposition and reconstruction filter banks. All the filtration methods described here are tested on real ECG records with additive myopotential noise character and are implemented in the Matlab environment.
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Automatic detection of K-complexes in sleep EEG signals
Pecníková, Michaela ; Ronzhina, Marina (referee) ; Kozumplík, Jiří (advisor)
This paper addresses the problem of detecting K-complexes in sleep EEG. The study of sleep has become very essential to diagnose the brain disorders and analysis of brain activities. Since Kcomplex can have a wide variety of shapes it is very difficult to detect the K-complexes manually. In this paper, I present an automatic method for K-complexes detection based wavelet transform,TKEO and method for classification using feedforward multilayer neural network designed in Matlab. Detection performance reached the value approx. from 52,9 to 83,6 %.
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Detection of QRS complex in experimental ECG data
Bucsuházy, Kateřina ; Ronzhina, Marina (referee) ; Vítek, Martin (advisor)
This Bachelor work deals with QRS complex detection. The theoretical part of the presented work is focused on summary of selected methods of QRS complex detection. There is described in detail wavelet transform, which is used for realization of QRS complex detector in Matlab. Specifically is used redundant dyadic discrete wavelet transform and biorthogonal wavelet with odd symmetry bior1.5. The algorithm was evaluated on the standard CSE database. The detector was modified to be able to detect QRS complexes in experimental data.
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Wavelet Filtering of ECG Signals
Handl, Marek ; Smital, Lukáš (referee) ; Kozumplík, Jiří (advisor)
The work deals with the wavelet transformation, focusing on wavelet transforms with discrete time (DTWT). The practical part is focused on the implementation of redundant packet DTWT and its use in the filtration of ECG signals. The main part of the work is to design wiener filter that uses redundant packet DTWT, designed to eliminate interference myopotentials of ECG signals. The actual solution is implemented in Matlab. Testing is performed on the library CSE using noise model myopotentials used to noising original signals. For optimum parameters designed filter is used the genetic algorithm (GA). The work is carried out comparing the proposed filter redundant packet DTWT a variant of redundant dyadic DTWT.
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Processing of images of early spruce needles scanned by MR technology
Raichl, Jaroslav ; Říha, Kamil (referee) ; Gescheidtová, Eva (advisor)
This semester project deals with filtering of the images detected by use of NMR obtained by NMR application measurement of nuclear magnetic resonance (NMR). This thesis includes the theory of nuclear magnetic resonance, digital filters, basic digital filter banks structures, theory of Wavelet transformation and description of Signal to Noise Ratio calculation. Basic procedure of the MR signal denoising is summarized in the theoretical part of the thesis. The denoising of the images detected by nuclear magnetic resonance is described. In experimental part filtering methods for images denoising are described, which are implemented in program Matlab. These methods are based on Wavelet transformation, digital filter banks with proper thresholding. Effectiveness of filtering methods designed was verified on 2D NMR images. All of these 2D images were measure on MR tomography in the Institute of Scientific Instruments Academy of Science of the Czech Republic in Brno.
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JAVA-based effective implementation of an image compression tool
Průša, Zdeněk ; Rajmic, Pavel (referee) ; Malý, Jan (advisor)
This diploma thesis deals with digital image lossy compression. Lossy compression in general inserts some kind of distorsion to the resulting image. The distorsion should not be interupting or even noticable in the better case. For image analysis there is used process called transformation and for choosing relevant coefficients process called coding. Evaluation of image quallity can be done by objective or subjective method. There is encoder introduced and realized in this work. Encoder utilizes two-dimension wavelet transform and SPIHT algortihm for coefficient coding. It was made use of accelerated method of wavelet transform computation by lifting scheme. Coder can proccess color information of images using modificated original SPIHT algorithm. For implementation the JAVA programming language was employed. The object-oriented design principes was made use of and thus the program is easy to extended. At demonstaration pictures there are shown effectiveness and characteristic way of distorsion of the proposed coder at high compression rates.
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Processing of iris images for biometric applications
Osičková, Kristýna ; Drahanský, Martin (referee) ; Kolář, Radim (advisor)
Biometrics is a method of recognizing the identity of a person based on unique biological characteristics that are unique to each person. The methods of biometric identification is currently becoming increasingly widespread in various sectors. This work is focused on the identification of a person by iris images. The introductory section describes the principles of the well-known methods for biometric applications and the next part describes the design method and its implementation in Matlab. In the practical part, fast radial symmetry method is used for detection of pupil, from which it derives further image processing. Two dimensional discrete welvet transform is used here. The proposed algorithm is tested on databases CASIA-Iris- Interval and database IITD.
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Muscle noise filtering in ECG signals
Novotný, Jiří ; Kubičková, Alena (referee) ; Smital, Lukáš (advisor)
This master's thesis deals with the optimization of numerical coefficients of the Wiener filter for muscle noise filtering in ECG signals. The theoretical part deals with ECG signal characteristic and muscle interference. It also contains a summary of the wavelet transform, wavelet Wiener's filtration, methods for calculating of the threshold and thresholding. In the last theoretical part the characteristic optimization techniques, the exhausive search and Nelder-Mead simplex method are mentioned, which were implemented in the practical part of this thesis in MATLAB. The functional verification and Wiener's filter optimization were tested on the standard electrocardiograms database CSE. By using the methods of exhausive search, the initial estimate for the solution method Nelder-Mead was obtained. The optimization method Nelder-Mead gives better results in the orders of hundredths or tenths than the method of exhausive search. The practical part is finished by the comparison of results of implemented algorithm with optimum coefficients, implemented in this thesis, with the results of other methods for filtering muscle interference in ECG signals.
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Boundary effects in signal processing: From classical problems to new opportunities
Popoola, Seyi James ; Druckmüllerová, Hana (referee) ; Cicone, Antonio (advisor)
Tato práce se zaměřuje na hraniční problémy při rozkladu signálů. Problematika hranic klasických metod byla rozsáhle studována, ale v posledních několika desetiletích byla představena nová generace metod. Implementace těchto nových metod má potenciál produkovat efektivním způsobem přesnější, flexibilnější a interpretovatelné výsledky, které mohou pomoci posunout výzkum v reálných aplikacích v různých oblastech. Hraniční problémy pro tyto techniky byly studovány teprve nedávno. Dosud zveřejněné výsledky ukazují, že tyto metody mají svá omezení a předpoklady, které je třeba pečlivě zvážit, aby se předešlo možnému zneužití. V této studii identifikujeme a řešíme hlavní překážky spojené s používáním těchto nových metod a poskytujeme také doporučení, jak je co nejefektivněji využít. Abychom dále ilustrovali možné důsledky nesprávného použití, provádíme komplexní zkoumání jejich aplikace na skutečná data a provádíme numerické simulace. Nakonec navrhujeme soubor osvědčených postupů pro optimalizaci výkonu těchto technik v kontextu rozkladu signálu. Zásadním návrhem je použít před aplikací jakékoli metody rozkladu techniku rozšíření signálu jako prostředek ke zmírnění hraničních efektů.
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