Deep learning, usually built upon artificial neural networks, was proposed in 1943, but poor computational capability restricted its development at that time. With the advancement of computer architecture and chip design, deep learning gains sufficient computational power and has revolutionized many areas in computer vision. As a fundamental research area of computer vision, video enhancement often serves as the first step of many modern vision systems and facilitates numerous downstream vision tasks. This thesis provides a comprehensive study of video enhancement, especially in the sense of video frame interpolation and space-time video super-resolution.
For video frame interpolation, two novel methods, named GDConvNet and VFIT, are proposed. In GDConvNet, a novel mechanism named generalized deformable convolution is introduced in order to overcome the inaccuracy flow estimation issue in the flow-based methods and the rigidity issue of kernel shape in the kernel-based methods. This mechanism can effectively learn motion information in a data-driven manner and freely select sampling points in space-time. Our GDConvNet, built upon this mechanism, is shown to achieve state-of-the-art performance. As for VFIT, the concept of local attention is firstly introduced to video interpolation, and a novel space-time separation window-based self-attention scheme is further devised, which not only saves costs but acts as a regularization term to improve the performance.
Based on the new scheme, VFIT is presented as the first Transformer-based video frame interpolation framework. In addition, a multi-scale frame synthesis scheme is developed to fully realize the potential of Transformers. Extensive experiments on a variety of benchmark datasets demonstrate the superiority and liability of VFIT.
For space-time video super-resolution, a novel unconstrained space-time video super-resolution network is proposed to solve the common issues of the existing methods that either fail to explore the intrinsic relationship between temporal and spatial information or lack flexibility in the choice of final temporal/spatial resolution. To this end, several new ideas are introduced, such as integration of multi-level representations and generalized pixshuffle. Various experiments validate the proposed method in terms of its complete freedom in choosing output resolution, as well as superior performance over the state-of-the-art methods. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28182 |
| Date | January 2022 |
| Creators | Shi, Zhihao |
| Contributors | Chen, Jun, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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