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Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data
Hrbáček, Radek ; Zátyik, Ján (referee) ; Rajmic, Pavel (advisor)
This thesis deals with the nuclear magnetic resonance field, especially spectroscopy and spectroscopy imaging, sparse signal representation and low-rank approximation approaches. Spectroscopy imaging methods are becoming very popular in clinical praxis, however, long measurement times and low resolution prevent them from their spreading. The goal of this thesis is to improve state of the art methods by using sparse signal representation and low-rank approximation approaches. The compressed sensing technique is demonstrated on the examples of magnetic resonance imaging speedup and hyperspectral imaging data saving. Then, a new spectroscopy imaging scheme based on compressed sensing is proposed. The thesis deals also with the in vivo spectrum quantitation problem by designing the MRSMP algorithm specifically for this purpose.
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Restoration of overexposed digital image
Zonygová, Kristýna ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The Bachelor thesis deals with the recovery of overexposed grayscale images based on sparse signal representation. For image conversion to another representation wavelet transform was used. In this representation, the solution of convex optimization which demand on relax signal sparse and restored pixels values was searched for. In this case, the proximal Douglas-Rachford algorithm was applied which uses two proximal operators. The implementation was carried out in numerical computing enviroment MATLAB using the Wavelet Toolbox software. The PSNR (peak signal-to-noise ratio) was utilized to evaluate success rate of proposed method. The method was tested on 5 random images and compared to results of image manipulation software Adobe Photoshop Lightroom CC.
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Restoration of overexposed digital image
Zonygová, Kristýna ; Veselý, Vítězslav (referee) ; Rajmic, Pavel (advisor)
The Bachelor thesis deals with the recovery of overexposed grayscale images based on sparse signal representation. For image conversion to another representation wavelet transform was used. In this representation, the solution of convex optimization which demand on relax signal sparse and restored pixels values was searched for. In this case, the proximal Douglas-Rachford algorithm was applied which uses two proximal operators. The implementation was carried out in numerical computing enviroment MATLAB using the Wavelet Toolbox software. The PSNR (peak signal-to-noise ratio) was utilized to evaluate success rate of proposed method. The method was tested on 5 random images and compared to results of image manipulation software Adobe Photoshop Lightroom CC.
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Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data
Hrbáček, Radek ; Zátyik, Ján (referee) ; Rajmic, Pavel (advisor)
This thesis deals with the nuclear magnetic resonance field, especially spectroscopy and spectroscopy imaging, sparse signal representation and low-rank approximation approaches. Spectroscopy imaging methods are becoming very popular in clinical praxis, however, long measurement times and low resolution prevent them from their spreading. The goal of this thesis is to improve state of the art methods by using sparse signal representation and low-rank approximation approaches. The compressed sensing technique is demonstrated on the examples of magnetic resonance imaging speedup and hyperspectral imaging data saving. Then, a new spectroscopy imaging scheme based on compressed sensing is proposed. The thesis deals also with the in vivo spectrum quantitation problem by designing the MRSMP algorithm specifically for this purpose.
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