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Neural Networks for Video Quality Enhancement
Sirovatka, Matej ; Juránek, Roman (oponent) ; Hradiš, Michal (vedoucí práce)
In this thesis, a new method for video super-resolution is proposed. The method is based on the idea of using deformable convolutional layers together with optical flow to align features from multiple sequential video frames. This novel module is then used in a U-Net-like deep neural network to predict high-resolution frames. The proposed method is evaluated on a dataset containing real-life scenes and compared to other methods. Multiple different configurations of the proposed method are tested and the results are analyzed. The results of the experiments show promising results, with the model outperforming bilinear interpolation, and single-frame methods. Multiple different architectures of the feature alignment module together with the rest of the U-Net architecture are tested, showing that using Vgg19 as the encoder of the U-Net gives the best results.

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1 Sirovátka, Michal
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