Boosting Structure Consistency for Multispectral and Multimodal Image Registration

Abstract

Multispectral imaging plays a vital role in the area of computer vision and computational photography. As spectral band images can be misaligned due to imaging device movement or alternation, image registration is necessary to avoid spectral information distortion. The current registration measures specialized for multispectral data are typically robust yet complex, requiring excessive computation. The common measures such as sum of squared differences (SSD) and sum of absolute differences (SAD) are computationally efficient whereas they perform poorly on multispectral data. To cope with this challenge, we propose a structure consistency boosting (SCB) transform that aims at boosting the structural similarity of multispectral images. With SCB, the common measures can be employed for multispectral image registration. The SCB transform exploits the fact that inherent edge structures maintain relative saliency locally despite the nonlinear variation between band images. A statistical prior of the natural image, which is based on the gradient-intensity correlation, is explored to build a parametric form of SCB. Experimental results validate that the SCB transform outperforms current similarity enhancement algorithms, and performs better than the state-of-the-art multispectral registration measures. Thanks to the generality of the statistical prior, the SCB transform is also applicable to various multimodal data such as flash/no-flash images and medical images.