We propose multispectral and multimodal image registration using a dynamic fusion index. The dynamic fusion index represents the fusion degree of two pair images, and we adopt it for multispectral and multimodal image registration. Moreover, adaptive weights represent confidence on structural features of pair images, and we utilize them to select reliable components of pair images for image registration. Based on the adaptive weights, we present adaptive weighted total variation to calculate the similarity between the reference and target images. In the adaptive weighted total variation framework, we first get the dynamic fusion index by fixing transformation parameters, and then estimate transformation parameters by fixing the dynamic fusion index. Finally, we determine the transformation parameters for image registration by iterating up to the convergence, and align the target image based on them. Experimental results demonstrate that the proposed method outperforms state-of-the-art registration ones in terms of visual quality and quantitative measurements.