Dense Cross-Scale Image Alignment With Fully Spatial Correlation and Just Noticeable Difference Guidance

TL;DR

This paper introduces a dense cross-scale image alignment model that leverages spatial correlation and perceptual sensitivity to improve accuracy and efficiency in unsupervised image alignment tasks.

Contribution

The proposed model uniquely combines cross-scale feature correlations with a fully spatial correlation module and just noticeable difference guidance for enhanced alignment performance.

Findings

Outperforms state-of-the-art methods in accuracy

Supports flexible accuracy-efficiency trade-offs

Achieves low computational costs with high precision

Abstract

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.