Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
This addresses a critical bottleneck in applications like image registration and structure from motion where conventional methods fail with multimodal images.
The paper tackles the problem of matching multimodal images under challenging conditions including noise, scale changes, rotation, and nonlinear intensity distortion, achieving high-quality matches that outperform mainstream methods in evaluations.
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.