Dense Cross-Scale Image Alignment With Fully Spatial Correlation and Just Noticeable Difference Guidance
This work addresses image alignment challenges for computer vision applications, representing an incremental improvement with novel components.
The paper tackles the problem of limited accuracy and high computational complexity in unsupervised image alignment by proposing a dense cross-scale model with a fully spatial correlation module and just noticeable difference guidance, achieving superior performance over state-of-the-art methods in experiments.
Existing unsupervised image alignment methods exhibit limited accuracy and high computational complexity. To address these challenges, we propose a dense cross-scale image alignment model. It takes into account the correlations between cross-scale features to decrease the alignment difficulty. Our model supports flexible trade-offs between accuracy and efficiency by adjusting the number of scales utilized. Additionally, we introduce a fully spatial correlation module to further improve accuracy while maintaining low computational costs. We incorporate the just noticeable difference to encourage our model to focus on image regions more sensitive to distortions, eliminating noticeable alignment errors. Extensive quantitative and qualitative experiments demonstrate that our method surpasses state-of-the-art approaches.