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Filtering methods for NMR measurements
Nezhyba, Jiří ; Mikulka, Jan (referee) ; Gescheidtová, Eva (advisor)
This master’s thesis deals with the wavelet transform and its use in processing and removing noise from images acquired by nuclear magnetic resonance. It defines fundamental terms for this work as mother wavelet or thresholding. Above all, it describes the principle of wavelet transform, thresholding techniques and criteria for evaluating the effectiveness of filtration. It describes the relation between wavelet transforms and digital filter banks. The experimental section describes the designed filtering method for removing noise from an image captured by the technique of nuclear magnetic resonance. We applied to different kinds of mother wavelets. Evaluation of the effectiveness of filtering was performed using the signal to noise ratio, relative contrast and the steepness of the intensity changes in signal intensity. It also discusses the comparison of properties of the image and selecting the mother wavelets based on image characteristics. Images were compared in terms of a histogram, cumulative histogram, k-space and the difference image.
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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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Recognition by Retina
Stružka, Jaroslav ; Orság, Filip (referee) ; Fiala, Jiří (advisor)
This thesis deals with recognition by retina (identification, verification). In introduction we describe information about human eye, its diseases with focus on retina impact. Further (in main part) we conduct SW analyses based on biometry retina requirements and design of SW application for retina recognition. It is based on processing pipeline design (sequential application of image filters). This pipeline mostly contains filters focused on edge detection, adaptive threshold and skeletonisation. Finally, basic SW functions includes users registration (enroll), identification, verification. In conclusion we discuss experimental results and success of designed SW in practical application.
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Filtering methods for MR images processing
Pláněk, Jiří ; Smékal, Zdeněk (referee) ; Gescheidtová, Eva (advisor)
This master´s thesis deals with wavelet transformation and its signal and image noise reduction application method. Significant parameters problems as a wavelet type, a threshold technique selection, a threshold level and a level analysis selection for successful signal and noise image filtering are described. A relation between wavelet transformation and digital bank filter is used by anti-noise and sub-bandwidth filtration. A part of the master´s thesis is focused on nuclear magnetic resonation, where jaw-joint image is processed. Jaw joint image noise reduction filtration methods are used in experimental part of the master´s thesis. Consequently, filtration methods improve a jaw joint image quality, which helps a doctor with patient health state condition. Different types of wavelets were tested and in different application methods order. Filtration methods results were visually compared; therefore any conclusion comparison has subjective matter. Accordingly, only doctor is able to resolve which filtration method is convenient to use to determine patient health state.
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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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Removal of a Known Signal from a Recording
Urbánek, Radomír ; Žmolíková, Kateřina (referee) ; Černocký, Jan (advisor)
The goal of this diploma thesis is to design and implement method for removing known signal from recorded sound. In the introductory part of the thesis are described the properties of sound and its propagation through the space, digital signal processing on the computer and the computing of the system impulse response. The sequential development of the methods leading to the removal of the known signal from the recording is described further. The following part contains a description and implementation of this method. It also describes how and at what data the method will be tested. Finally, testing is evaluated, improvements and further possible work is proposed.
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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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Image data processing using principal component analysis (PCA)
Solnický, Jan ; Archalous, Tomáš (referee) ; Rychtárik, Milan (advisor)
This project deals with using of principal component analysis (PCA) in image processing and its aim is introduce mathematical apparatus of principal component analysis and possibility of its using in image processing. Project contains instructions how to compress images with using PCA and also how to convert colour image to grayscale intensity image. There are shown how to use PCA to denoising operation in wavelet spectrum. Project includes results of that operations and their evaluation.
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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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