GVTNet: Graph Vision Transformer For Face Super-Resolution
This work addresses face super-resolution for applications like image enhancement, but it is incremental as it builds on existing transformer architectures by incorporating graph-based interactions.
The paper tackles the problem of distorted facial components in face super-resolution by proposing GVTNet, a graph vision transformer that models patch relationships using graph neural networks, achieving superior results over state-of-the-art methods as shown in quantitative and visualization experiments.
Recent advances in face super-resolution research have utilized the Transformer architecture. This method processes the input image into a series of small patches. However, because of the strong correlation between different facial components in facial images. When it comes to super-resolution of low-resolution images, existing algorithms cannot handle the relationships between patches well, resulting in distorted facial components in the super-resolution results. To solve the problem, we propose a transformer architecture based on graph neural networks called graph vision transformer network. We treat each patch as a graph node and establish an adjacency matrix based on the information between patches. In this way, the patch only interacts between neighboring patches, further processing the relationship of facial components. Quantitative and visualization experiments have underscored the superiority of our algorithm over state-of-the-art techniques. Through detailed comparisons, we have demonstrated that our algorithm possesses more advanced super-resolution capabilities, particularly in enhancing facial components. The PyTorch code is available at https://github.com/continueyang/GVTNet