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Methods for increasing bit-depth in images
Záviška, Pavel ; Koldovský, Zbyněk (referee) ; Rajmic, Pavel (advisor)
The goal of this bachelor thesis is the application of an issue of sparse representations on the task of increasing bit-depth in images. Standard methods for bit-depth expansion are described, subsequently a method using sparse representation of an image signal is introduced. Methods are programmed in the Matlab enviroment. The results of the methods implemented are compared by using PSNR, SSIM objective indexes and subjectively using an online questionnaire.
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Interactive web applications supporting education
Kratoš, Filip ; Koldovský, Zbyněk (referee) ; Rajmic, Pavel (advisor)
This term paper is focused on design of web applets using Javascript. It deals with design of six applets works with one-dimensional signals, especially audio-signals. The applets are Combination of Signals, Summation of Sine and Cosine Functions, Signal Filtering, Effect of Phase on Audio-signal, Effect of Aliasing, and Linear and Nonlinear Systems. Main themes of the paper are theoretical background for applets, implementation of one applet and design of the interfaces of other applets.
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Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Šembera, Ondřej ; Tichavský, Petr ; Koldovský, Zbyněk
In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.
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Interactive web applications supporting education
Kratoš, Filip ; Koldovský, Zbyněk (referee) ; Rajmic, Pavel (advisor)
This term paper is focused on design of web applets using Javascript. It deals with design of six applets works with one-dimensional signals, especially audio-signals. The applets are Combination of Signals, Summation of Sine and Cosine Functions, Signal Filtering, Effect of Phase on Audio-signal, Effect of Aliasing, and Linear and Nonlinear Systems. Main themes of the paper are theoretical background for applets, implementation of one applet and design of the interfaces of other applets.
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Methods for increasing bit-depth in images
Záviška, Pavel ; Koldovský, Zbyněk (referee) ; Rajmic, Pavel (advisor)
The goal of this bachelor thesis is the application of an issue of sparse representations on the task of increasing bit-depth in images. Standard methods for bit-depth expansion are described, subsequently a method using sparse representation of an image signal is introduced. Methods are programmed in the Matlab enviroment. The results of the methods implemented are compared by using PSNR, SSIM objective indexes and subjectively using an online questionnaire.
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Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Tichavský, Petr ; Šembera, Ondřej ; Koldovský, Zbyněk
Modeling real-world acoustic signals and namely speech signals as piecewise stationary random processes is a possible approach to blind separation of linear mixtures of such signals. In this paper, the piecewise AR(1) modeling is studied and is compared to the more common piecewise AR(0) modeling, which is known under the names Block Gaussian SEParation (BGSEP) and Block Gaussian Likelihood (BGL). The separation based on the AR(0) modeling uses an approximate joint diagonalization (AJD) of covariance matrices of the mixture with lag 0, computed at epochs (intervals) of stationarity of the separated signals. The separation based on the AR(1) modeling uses the covariances of lag 0 and covariances of lag 1 jointly. For this model, we derive an approximate Cram´er-Rao lower bound on the separation accuracy for estimation based on the full set of the statistics (covariance matrices of lag 0 and lag 1) and covariance matrices with lag 0 only. The bounds show the condition when AR(1) modeling leads to significantly improved separation accuracy.
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