Master thesis deals with the study of ultrasound. The first part of this work is devoted to basic physical principles, imaging modes, ultrasound probes, biological effects and the last part is associated with the artifact associated with ultrasound waves. In the next part the specific noise called speckle noise is detaily discussed. The description includes both a mathematical and a statistical view. Several methods are described to eliminate this noise. In the practical part the described methods are realized and compared based on objective and subjective evaluation.

High quality image /video has become an integral part in our day-to-day life ranging from many areas of science, engineering and medical diagnosis. All these imaging applications call for high resolution, properly focused and crisp images. However, in real situations obtaining such a high quality image is expensive, and in some cases it is not practical. In imaging systems such as digital camera, blur and noise degrade the image quality. The recorded images look blurred, noisy and unable to resolve the finer details of the scene, which are clearly notable under zoomed conditions. The post processing techniques based on computational methods extract the hidden information and thereby improve the quality of the captured images. The study in this thesis focuses on deconvolution and eventually blind de-convolution problem of a single frame captured at low light imaging conditions arising from digital photography/surveillance imaging applications. Our intention is to restore a sharp image from its blurred and noisy observation, when the blur is completely known/unknown and such inverse problems are ill-posed/twice ill-posed. This thesis consists of two major parts. The first part addresses deconvolution/blind deconvolution problem using Bayesian approach with fuzzy logic based gradient potential as a prior functional. In comparison with analog cameras, artifacts are visible in digital cameras when the images are enlarged and there is a demand to enhance the resolution. The increased resolution can be in spatial, temporal or even in both the dimensions. Super resolution reconstruction methods reconstruct images/video containing spectral information beyond that is available in the captured low resolution images. The second part of the thesis addresses resolution enhancement of observed monochromatic/color images using multiple frames of the same scene. This reconstruction problem is formulated in Bayesian domain with an aspiration of reducing blur, noise, aliasing and increasing the spatial resolution. The image is modeled as Markov random field and a fuzzy logic filter based gradient potential is used to differentiate between edge and noisy pixels. Suitable priors are adaptively applied to obtain artifact free/reduced images. In this work, all our approaches are experimentally validated using standard test images. The Matlab based programming tools are used for carrying out the validation. The performance of the approaches are qualitatively compared with results of recently proposed methods. Our results turn out to be visually pleasing and quantitatively competitive.

This master's thesis contains introduction to image filtration problems, especially to theoretical outlets, whose origin lies in linear systems theory and mathematical function analysis. There are described some approaches and methods which are used to image smoothing and for edge detection in an image. Mainly Sobel operator, Laplace operator and median filter are covered. The main contents of this project is discussion of some approaches of hardware acceleration of image filtering and design of time effective software and hardware implementations of filters in a form of program functions and combinational circuits using theoretical knowledges about time complexity of algorithms. Hardware and software implementation of named filters was done too. For every filter, time of filtration was measured and results were compared and analyzed.

Ultrasound investigation is one of meaningful imaging at present. Advantages of ultrasound are, that it hasn´t side effects as a rtg radiation and it is noninvasive. Ultrasound diagnostic is exploited in all branch of medicine (urology, cardiology, orthopeadist, gynecology etc.) to display organs, tissues and cavities of the human body. We use display in 2D, 3D and most modern display in 4D. We can encounter with many kinds of artifact. Artifacts are described more closely at 3rd chapter. Speckle noise deteriorates informative yiled of ultrasound picture. We try to remove speckle noise by simpler methods or more complex methods of filtration. These methods are described at 5th chapter. Programme for speckle filtering from pictures is part of this master´s thesis.

The master thesis is focused on the principle of ultrasonographs and their modes, and ways of denoising data obtained by ultrasonographs. This project will also concentrate on removing the speckle noise, because the speckle noise has the largest share of the noise in the measured data in ultrasonographs. The other main objective was to describe advanced methods of filtration, especially the modifications of the median filter, such as the modified hybrid median filter and modification of the anisotropic diffusion, namely generalizing Perona-Malik anisotropic diffusion using restricted dissimilarity functions. These two filters were tested on images with artificial noise and on data captured using the ultrasound system GE VingMed System FiVe. Also, the program for filtering and display filtered ultrasound data captured by GE VingMed System FiVe is described.

This work is about suggestion of the software for neural network based image segmentation. It defines basic terms for this topics. It is focusing mainly at preperation imaging information for image segmentation using neural network. It describes and compares different aproaches for image segmentation.