通过跨尺度关联与视觉敏感度引导，实现高效高精度图像对齐
Dense Cross-Scale Image Alignment With Fully Spatial Correlation and Just Noticeable Difference Guidance
- 利用跨尺度特征相关性降低对齐难度
- 在保持低计算开销下提升对齐精度
- 适合对图像质量敏感的视觉任务
现有无监督图像对齐方法存在精度有限和计算复杂度高的问题。为此，我们提出一种密集跨尺度图像对齐模型，通过考虑跨尺度特征间的相关性来降低对齐难度。该模型可通过调整使用尺度数量，灵活权衡精度与效率。此外，引入全空间相关模块，在维持低计算成本的同时进一步提升精度。通过引入可察觉差异（Just Noticeable Difference）机制，引导模型关注对失真更敏感的图像区域，从而消除明显的对齐误差。大量定量与定性实验表明，本方法优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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