# Normalized Total Gradient: A New Measure for Multispectral Image Registration

## TL;DR

This paper introduces the normalized total gradient (NTG), a novel measure for multispectral image registration that effectively handles local intensity variations and outperforms traditional methods.

## Contribution

The paper proposes NTG, a new similarity measure based on image gradients, specifically designed for multispectral images with intensity variations, and integrates it into a registration framework.

## Key findings

- NTG outperforms conventional registration measures.
- The method effectively handles local intensity variations.
- Experimental results validate the approach's effectiveness.

## Abstract

Image registration is a fundamental issue in multispectral image processing. In filter wheel based multispectral imaging systems, the non-coplanar placement of the filters always causes the misalignment of multiple channel images. The selective characteristic of spectral response in multispectral imaging raises two challenges to image registration. First, the intensity levels of a local region may be different in individual channel images. Second, the local intensity may vary rapidly in some channel images while keeps stationary in others. Conventional multimodal measures, such as mutual information, correlation coefficient, and correlation ratio, can register images with different regional intensity levels, but will fail in the circumstance of severe local intensity variation. In this paper, a new measure, namely normalized total gradient (NTG), is proposed for multispectral image registration. The NTG is applied on the difference between two channel images. This measure is based on the key assumption (observation) that the gradient of difference image between two aligned channel images is sparser than that between two misaligned ones. A registration framework, which incorporates image pyramid and global/local optimization, is further introduced for rigid transform. Experimental results validate that the proposed method is effective for multispectral image registration and performs better than conventional methods.