Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow |
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| Affiliation: | 1. Tianjin Key Lab. of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin 300072, China;2. Research Institute of Future Media Computing, Shenzhen University, Shenzhen 518060, China;1. Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense, Sichuan University, Chengdu 610064, PR China;2. School of Aeronautics and Astronautics, Sichuan University, Chengdu 610064, PR China;3. School of Computer Science and Engineering, Sichuan University, Chengdu 610064, PR China;1. School of Computer Science and Technology, Xidian University, Xi?an, China;2. School of Telecommunication Engineering, Xidian University, Xi?an, China |
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| Abstract: | This paper proposes a new video super-resolution method based on feature-guided variational optical flow. The key-frames are automatically selected and super-resolved using a method based on sparse regression. To overcome the blocking artifacts and deal with the case of small structures with large displacement, an efficient method based on feature-guided variational optical flow is used to super-resolve the non-key-frames. Experimental results show that the proposed method outperforms the existing benchmark in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The average PSNR improvement from the bi-cubic interpolation is 7.15 dB for four datasets. Thanks to the flexibility of designed automatic key-frame selection and the validness of feature-guided variational optical flow, the proposed method is applicable to various practical video sequences. |
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| Keywords: | Video super-resolution Feature-guided variational optical flow Key-frames PSNR |
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