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MR image processing
Mrákava, Petr ; Smékal, Zdeněk (referee) ; Gescheidtová, Eva (advisor)
The subject of this bachelor's thesis is to become familiar with the methods of processing image acquired by techniques of nuclear magnetic resonance. It describes the gradual steps in the digitizing of the signal to the image data. But in the process a disturbing component is almost always created in the image, in particular noise, which causes image devaluation. Therefore, further work is focused mainly on eliminating present disturbing components. For the noise elimination, the widely used wavelet transformation is applied, which is implemented by banks of digital filters. The experimental part of this work deals with design of MR filtering method, optimal filtering parameters setting, decomposition of images into magnetic field map, and subsequent comparison of results obtained.
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Lossless Image Compression Using Wavelet Transform
Tumpach, Jiří ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This work focuses on wavelet transform and its use in image compression particularly on a comparation between classical tensor product wavelets and new kind of second generation wavelet also known as red-black wavelet transform. Although brief comparison of EBCOT modifications, color transforms, wavelets and predictors are discussed too. A framework for an evaluation of some current methods is constructed and results across different image groups are presented. In addition, C++ library was created. Proposed lossless compression methods are better then JPEG 2000 and PNG.
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X-ray image analysis to remove disturbing artifacts for security applications
Schiller, Vojtěch ; Mezina, Anzhelika (referee) ; Burget, Radim (advisor)
This work deals with the issue of the decomposition of a composite X-ray image, on which both key informational and noise components are present simultaneously. The goal is to remove the present disturbing artifacts as repeating phenomena in the background using deep learning techniques while emphasizing the precise preservation of the informational components contained in the image. To achieve this, the convolutional neural network U-Net and its improved versions, which dominate especially in image segmentation, were used. Competitive models achieving excellent results at image-denoising tasks were also trained and compared. This work proposes a novel method, which was compared with the most modern architectures on the same dataset, and which, in the results, objectively and subjectively significantly surpassed all of them.
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Lossless Image Compression Using Wavelet Transform
Tumpach, Jiří ; Polok, Lukáš (referee) ; Bařina, David (advisor)
This work focuses on wavelet transform and its use in image compression particularly on a comparation between classical tensor product wavelets and new kind of second generation wavelet also known as red-black wavelet transform. Although brief comparison of EBCOT modifications, color transforms, wavelets and predictors are discussed too. A framework for an evaluation of some current methods is constructed and results across different image groups are presented. In addition, C++ library was created. Proposed lossless compression methods are better then JPEG 2000 and PNG.
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MR image processing
Mrákava, Petr ; Smékal, Zdeněk (referee) ; Gescheidtová, Eva (advisor)
The subject of this bachelor's thesis is to become familiar with the methods of processing image acquired by techniques of nuclear magnetic resonance. It describes the gradual steps in the digitizing of the signal to the image data. But in the process a disturbing component is almost always created in the image, in particular noise, which causes image devaluation. Therefore, further work is focused mainly on eliminating present disturbing components. For the noise elimination, the widely used wavelet transformation is applied, which is implemented by banks of digital filters. The experimental part of this work deals with design of MR filtering method, optimal filtering parameters setting, decomposition of images into magnetic field map, and subsequent comparison of results obtained.
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