Video super-resolution (VSR) aims to give a satisfying estimation of a high-resolution (HR) image from multiple similar low-resolution (LR) images by exploiting their hidden redundancy. The rapid development of convolutional neural network (CNN) techniques provide numerous new possibilities to solve the VSR problem. Recent VSR methods combine CNN with motion compensation to cancel the inconsistencies among the LR images and merge them to an HR images. To compensate the motion, pixels in input frames are warped according to optical-flow-like information. In this procedure, trade-off has to be made between the distraction caused by spatio-temporal inconsistencies and the pixel-wise detail damage caused by the compensation. We proposed a novel VSR method with the name, Video Super-Resolution via Dynamic Local Filter Network, and its upgraded edition, Video Super-Resolution with Compensation in Feature Extraction. Both methods perform motion compensation via a dynamic local filter network, which processes the input images with dynamically generated filter kernels. These kernels are sample-specific and position-specific. Therefore, our proposed methods can eliminate the inter-frame differences during feature extractions without explicitly manipulating pixels. The experimental results demonstrate that our methods outperform the state-of-the-art VSR algorithms in terms of PSNR and SSIM and recover more details with superior visual quality.

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Today Image acquisition equipment produces huge amounts of data that needs to be processed. Often the data describes signals with a dimensionality higher then 2, as with ordinary images. This introduce a problem when it comes to process this high dimensional data since ordinary signal processing tools are no longer suitable. New faster and more efficient tools need to be developed to fully exploit the advantages with e. g. a 3D CT-scan. One such tool is filternets, a layered networklike structure, which the signal propagates through. A filternet has three fundamental advantages which will decrease the filtering time. The network structure allows complex filter to be decomposed into simpler ones, intermediate result may be reused and filters may be implemented with very few nonzero coefficients (sparse filters). The aim of this study has been to create an implementation for filternets and optimize it with respect to execution time. Specially the possibility to use filternets that approximates a harmonic filterset for estimating orientation in 3D signals is investigated. Tests show that this method is up to about 30 times faster than a full filterset consisting of dense filters. They also show a slightly larger error in the estimated orientation compared with the dense filters, this error should however not limit the usability of the method.

In the thesis there are described the implementations of various types of filters to remove disturbing signals, which often degrade the ECG signal. In particular, it is a zero isoline fluctuations and power network interference. It is used a principle of the Lynn’s linear filters. The individual filters are designed in a recursive and non-recursive implementation. Then there is described a time-varying linear Lynn's filter for removing drift of zero isoline signal. The thesis also includes filters with minimized calculating time of response, by sampling rate conversion method for both interference types. In conclusion there is an experimental study of the filter implementation for ECG signal with false and real interferences.