Video super-resolution is a technology that enhances the resolution of low-quality videos, making them look sharper and more detailed. In this article, we propose a novel approach called Semantic Lens, which leverages the power of semantics to improve video super-resolution. We decouple the video into instances, events, and scenes, and embed these semantic categories into the features extracted from low-resolution (LR) frames. This allows for instance-centric inter-frame alignment and boosts the performance of video super-resolution.
Semantic Extractor
The core of Semantic Lens is the Semantic Extractor, which breaks down the video into instances, events, and scenes. We construct a latent semantic space where each frame is represented as a point cloud, and instances, events, and scenes are symbolized as clusters of points. This allows us to capture the semantic meaning of each frame and align them with each other.
Global Perspective Shifter
To bridge the gap between the semantic priors and pixel-level features, we develop a Global Perspective Shifter (GPS). GPS shifts the perspective of the features from the instance level to the global level, allowing us to capture the contextual information and align the instances across frames.
Instance-Specific Semantic Embedding Encoder
To ensure accurate inter-frame alignment, we design an Instance-Specific Semantic Embedding Encoder (ISEE). ISEE embeds the semantic priors into the features extracted from LR frames in a position-embedding-like manner. This allows us to capture the instance-specific semantics and align them with each other across frames.
Semantics-Powered Attention Cross-Embedding
The Semantics-Powered Attention Cross-Embedding (SPACE) block is the core of our method. It combines the global perspective shifter and the instance-specific semantic embedding encoder to align the instances across frames. SPACE uses attention mechanism to focus on the relevant regions and embed the semantics into the features.
Experiments
We evaluate Semantic Lens on several benchmark datasets and show that it consistently outperforms state-of-the-art methods. Our method not only improves resolution but also generates more enjoyable visual results.
Conclusion
In this article, we proposed Semantic Lens, a novel approach to video super-resolution that leverages the power of semantics to improve performance. By decoupling the video into instances, events, and scenes, and embedding these semantic categories into features, we enable instance-centric inter-frame alignment and boost the performance of video super-resolution. Semantic Lens outperforms state-of-the-art methods and generates more enjoyable visual results, demonstrating its effectiveness in unlocking the potential of video super-resolution.
