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http://hdl.handle.net/11375/24161
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DC Field | Value | Language |
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dc.contributor.advisor | Chen, Jun | - |
dc.contributor.author | Kong, Lingshi | - |
dc.date.accessioned | 2019-03-25T13:15:58Z | - |
dc.date.available | 2019-03-25T13:15:58Z | - |
dc.date.issued | 2018 | - |
dc.identifier.uri | http://hdl.handle.net/11375/24161 | - |
dc.description.abstract | Video super-resolution becomes significant desire recently to provide high-resolution contents for ultra high definition displays. Recent advances in video super-resolution have shown that convolutional neural networks combining with motion compensation, which can merge information from multiple low-resolution frames, to generate high-quality frames. But it has been demonstrated that most deep learning based video super-resolution methods heavily dependent on the accuracy of motion estimation and compensation. Other than before, here proposed a different end-to-end deep neural network that inexplicit compensates motion through the generates dynamic filters. The dynamic filters are computed depending on the local spatio-temporal neighborhood of each pixel. With this approach, a high-resolution frame has reconstructed directly from the low-resolution input frames by using a series networks combining with a dynamic local filter network. The proposed network can generate much sharper high-resolution videos with temporal consistency, compared to the previous methods. | en_US |
dc.language.iso | en | en_US |
dc.title | Video Super-Resolution | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Kong_Lingshi_2018Sep_M.A.Sc..pdf | 9.44 MB | Adobe PDF | View/Open |
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