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Robust Student estimator
Hlavinka, Radek ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
This Master's thesis deals with Bayesian approach to robust parameter estimation for ARX models. Robustness is achieved by assuming the measurement noise to be generated by Student-t distribution. The asumption of Student-t noise renders the model's posterior intractable and requires utilization of approximation techniques. This thesis considers algorithms using Gibbs sampler and Variational approximation and compares them with Ordinary Least Squares. The algorithms are compared based on their Maximum Likelihood estimation. It is shown that approaches assuming the Student-t noise perform better in simulation. The results from data acquired from physical system are however similar for all algorithms considered.
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Robust Student estimator
Rázek, Stanislav ; Friml, Dominik (referee) ; Dokoupil, Jakub (advisor)
The diploma thesis deals with the formulation of the algorithm for estimating the parameters of the linear ARX model with Student's noise using approximate Bayesian inference. The topics of Student's noise, Approximate Bayesian inference and Student's algorithm are discussed. The formulated parameter estimation algorithm is compared with other model parameter estimation methods and evaluated. At the same time, the Student's filter is derived and its connection with the Kalman filter is discussed.
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Image Deblurring in Demanding Conditions
Kotera, Jan ; Šroubek, Filip (advisor) ; Portilla, Javier (referee) ; Jiřík, Radovan (referee)
Title: Image Deblurring in Demanding Conditions Author: Jan Kotera Department: Institute of Information Theory and Automation, Czech Academy of Sciences Supervisor: Doc. Ing. Filip Šroubek, Ph.D., DSc., Institute of Information Theory and Automation, Czech Academy of Sciences Abstract: Image deblurring is a computer vision task consisting of removing blur from image, the objective is to recover the sharp image corresponding to the blurred input. If the nature and shape of the blur is unknown and must be estimated from the input image, image deblurring is called blind and naturally presents a more difficult problem. This thesis focuses on two primary topics related to blind image deblurring. In the first part we work with the standard image deblurring based on the common convolution blur model and present a method of increasing robustness of the deblur- ring to phenomena violating the linear acquisition model, such as for example inten- sity clipping caused by sensor saturation in overexposed pixels. If not properly taken care of, these effects significantly decrease accuracy of the blur estimation and visual quality of the restored image. Rather than tailoring the deblurring method explicitly for each particular type of acquisition model violation we present a general approach based on flexible automatic...
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Image Deblurring in Demanding Conditions
Kotera, Jan ; Šroubek, Filip (advisor) ; Portilla, Javier (referee) ; Jiřík, Radovan (referee)
Title: Image Deblurring in Demanding Conditions Author: Jan Kotera Department: Institute of Information Theory and Automation, Czech Academy of Sciences Supervisor: Doc. Ing. Filip Šroubek, Ph.D., DSc., Institute of Information Theory and Automation, Czech Academy of Sciences Abstract: Image deblurring is a computer vision task consisting of removing blur from image, the objective is to recover the sharp image corresponding to the blurred input. If the nature and shape of the blur is unknown and must be estimated from the input image, image deblurring is called blind and naturally presents a more difficult problem. This thesis focuses on two primary topics related to blind image deblurring. In the first part we work with the standard image deblurring based on the common convolution blur model and present a method of increasing robustness of the deblur- ring to phenomena violating the linear acquisition model, such as for example inten- sity clipping caused by sensor saturation in overexposed pixels. If not properly taken care of, these effects significantly decrease accuracy of the blur estimation and visual quality of the restored image. Rather than tailoring the deblurring method explicitly for each particular type of acquisition model violation we present a general approach based on flexible automatic...
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