Multimodal Image Matching based on Frequency-domain Information of Local Energy Response
This addresses robust image matching for applications like medical imaging or remote sensing, but it appears incremental as it builds on existing frequency-domain and feature detection techniques.
The paper tackles multimodal image matching challenges like nonlinear intensity differences and geometric distortions by proposing FILER, a method based on frequency-domain information of local energy response, which outperforms state-of-the-art algorithms in experiments.
Complicated nonlinear intensity differences, nonlinear local geometric distortions, noises and rotation transformation are main challenges in multimodal image matching. In order to solve these problems, we propose a method based on Frequency-domain Information of Local Energy Response called FILER. The core of FILER is the local energy response model based on frequency-domain information, which can overcome the effect of nonlinear intensity differences. To improve the robustness to local nonlinear geometric distortions and noises, we design a new edge structure enhanced feature detector and convolutional feature weighted descriptor, respectively. In addition, FILER overcomes the sensitivity of the frequency-domain information to the rotation angle and achieves rotation invariance. Extensive experiments multimodal image pairs show that FILER outperforms other state-of-the-art algorithms and has good robustness and universality.