  We present an effective method for the matching of multimodal images.
Accurate image matching is the basis of various applications, such as image
registration and structure from motion. Conventional matching methods fail when
handling noisy multimodal image pairs with severe scale change, rotation, and
nonlinear intensity distortion (NID). Toward this need, we introduce an image
pyramid strategy to tackle scale change. We put forward an accurate primary
orientation estimation approach to reduce the effect of image rotation at any
angle. We utilize multi-scale and multi-orientation image filtering results and
a feature-to-template matching scheme to ensure effective and accurate matching
under large NID. Integrating these improvements significantly increases noise,
scale, rotation, and NID invariant capability. Our experimental results confirm
the excellent ability to achieve high-quality matches across various multimodal
images. The proposed method outperforms the mainstream multimodal image
matching methods in qualitative and quantitative evaluations. Our
implementation is available at https://github.com/Zhongli-Fan/NISR.
