|
|
Determining the optimal patch size for sparse image representation
Šuránek, David ; Zátyik, Ján (referee) ; Špiřík, Jan (advisor)
Introduction of this thesis is dedicated to the description of basic concepts and algorithms for image processing using sparse representation. Furthermore there is mentioned neural network model called Restricted Boltzmann machine, which is in the practical part of the thesis subject of behaving observation in the task of determining the optimal block size for extrapolation using K-SVD algorithm
|
|
|
Sparse Representation for Classification of Posture in Bed
Mesárošová, Michaela ; Mihálik, Ondrej
Redundant dictionaries, also known as frames, offera non–orthogonal representation of signals, which leads to sparsityin their representative coefficients. As this approach providesmany advantageous properties it has been used in various applicationssuch as denoising, robust transmissions, segmentation,quantum theory and others. This paper investigates the possibilityof using sparse representation in classification, comparing theachieved results to other commonly used classifiers. The differentmethods were evaluated in a real-world classification task inwhich the position of a lying patient has to be deduced basedon the data provided by a pressure mattress of 30×11 sensors.The investigated method outperformed most of the commonlyused classifiers with accuracy exceeding 92%, while being lessdemanding on design and implementation complexity.
|
|
|
Alternative JPEG image decoder
Bureš, Jiří ; Štarha, Pavel (referee) ; Rajmic, Pavel (advisor)
This thesis deals with the JPEG image codec, edge detection in images, sparse signal representations and proximal algorithms. First, the operation of the JPEG encoder and decoder and the theory underlying it are described. Then, based on the theoretical knowledge, a new proximal algorithm is constructed and implemented in an existing JPEG algorithm in order to remove block relics in the decoded image. The programming side is solved in Matlab environment. The results are evaluated using MSE, PSNR and SSIM methods.
|
|
|
Audio signal restoration using the Plug-and-Play method
Švento, Michal ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
The topic of this thesis is the reconstruction of a digital audio signal that is corrupted in two ways, sample dropout and added noise. The classical approach to solving these problems are convex optimization algorithms, which are based on the sparsity of the audio signal. In this thesis, we try a new Plug-and-Play method that embeds a deep network, the denoiser, into conventional algorithms and attempt to solve these two distinct problems using a single algorithm. At the end of the paper, the algorithms are implemented and tested with the most common metrics and these results are evaluated. Modern methods have shown us that they can be more variable in the choice of parameters and offer more balanced solutions.
|
|
|
Sparse Representation of Signals
Mesárošová, Michaela ; Arm, Jakub (referee) ; Jirgl, Miroslav (advisor)
People who are immobile or lie for long periods are at high risk of developing pressure ulcers and require additional care. Therefore, it is necessary to monitor the condition of such persons as simply and efficiently as possible. In this work, we focus on processing the signals provided by a pressure mattress with a 30x11 sensor grid on which a person lays and the possibilities of its use after conversion into sparse representation coefficients. Redundant dictionaries, also known as frames, enable non-orthogonal representation of signals, which leads to a sparse representation of coefficients. Since this approach provides many advantageous properties and is being used in various applications, such as denoising, segmentation, robust transformations, quantum theory, and others, we verified the possibility of classifying a person’s lying position based on a sparse representation. The results were compared with other traditional classification methods, which were found to be less suitable for the classification problem, with the best-achieved result of 92.41 % for CNN, but with high demands on time, design and complexity. The success rate of the classification reached 92.76 %, with fewer demands on design and implementation complexity. The possibilities of classification and reconstruction of an image containing occlusions were also investigated, where the sparse representation proved to be an effective method to remove these defects.
|
|
|
Restoration of audio signals damaged by quantization
Šiška, Jakub ; Rajmic, Pavel (referee) ; Záviška, Pavel (advisor)
This master’s thesis deals with the restoration of audio signals damaged by quantization. The theoretical part starts with a description of quantization and dequantization in general, few existing methods of dequantization of audio signals and theory of sparse representations of signals are also presented. The next part introduces algorithms suitable for dequantization, specifically Douglas–Rachford, Chambolle–Pock, SPADEQ and implementation of these algorithms in MATLAB application in the next chapter. In the last part of this thesis, testing of reconstructed signals using the algorithms takes place and results are evaluated by objective measures SDR, PEMO-Q, PEAQ and subjective listening test MUSHRA.
|
|
|
Restoration of signals with limited instantaneous value for the multichannel audio signal
Hájek, Vojtěch ; Vrba, Kamil (referee) ; Záviška, Pavel (advisor)
This master’s thesis deals with the restoration of clipped multichannel audio signals based on sparse representations. First, a general theory of clipping and theory of sparse representations of audio signals is described. A short overview of existing restoration methods is part of this thesis as well. Subsequently, two declipping algorithms are introduced and are also implemented in the Matlab environment as a part of the thesis. The first one, SPADE, is considered a state- of-the-art method for mono audio signals declipping and the second one, CASCADE, which is derived from SPADE, is designed for the restoration of multichannel signals. In the last part of the thesis, both algorithms are tested and the results are compared using the objective measures SDR and PEAQ, and also using the subjective listening test MUSHRA.
|
|
|
Modern audio denoising with utilization of phase information
Skyva, Pavel ; Záviška, Pavel (referee) ; Rajmic, Pavel (advisor)
The thesis deals with modern methods of audio denoising. Reconstruction of the audiosignal is primarly based on utilization of phase information of signals and phase derivatives. Denoising methods also use sparse signal representations. In thesis is described the way of searching sparse coefficients using proximal Condat algorithm and following computation of reconstructed signal using this coefficients. The reconstruction algorithms are implemented in the MATLAB software with toolbox LTFAT included. Results of the reconstruction are compared using objective evaluation method Signal-to-Noise Ratio (SNR) and also by subjective evaluation.
|
|
|
Optimization of data representation for target tracking using sensor network
Cabalová, Klára ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The aim of this bachelor thesis is to find optimal data representation for target tracking using sensor network. There is described a model of decentralized sensor network and also the application of so called dictionary to represent the measured data. Also, there is theoretically introduced the K-SVD algorithm that is used for dictionary learning and there are learnt dictionaries for data representation based on the model signals. These dictionaries are compared with each other.
|
|
|
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.
|
guest ::
