Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor

TL;DR

This paper presents an end-to-end method for multi-spectral image registration and fusion, utilizing a novel edge descriptor for accurate alignment across different spectral modalities, resulting in high-quality fused images.

Contribution

Introduces a new edge descriptor-based registration algorithm for multi-spectral images, enabling precise alignment and fusion across spectral channels.

Findings

Achieves accurate registration in challenging scenarios

Produces high-quality fused images from multi-spectral data

Outperforms existing registration methods for multi-spectral images

Abstract

In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment of a level that allows us to further fuse the images. As our experiments show, we produce a high quality of multi-spectral image registration and fusion under many challenging scenarios.