In this paper, we propose an image fusion for open and unknown environments using normalized mutual information
(NMI) in an infrared (IR) and visual vision system. Image fusion is a field of study of image processing, and it creates a
new image to extract information from various different sensors. And also it gets effective information for a special
object. This can get object types, sensitive characteristic, and information which it not to get characteristic of object from
a single sensor. Image fusion in multi-sensors is two advantages. First, multi-sensor image has inherent redundancy for
each sensor because it can be fused each image from a various multi-band sensor. Second, multi-sensor differs from a
single sensor because it is included information of each sensor and is separated information of object easily in real
environments. Proposed method consists of extraction and comparison of feature point, image registration, and pseudo
color for display. Extraction of feature point is stage which it looks for a similar feature points between each sensor.
Then, the extraction of a similar feature point uses a corner detector. A detected correspondence point from multi-sensor
is compared feature point by using NMI. An acquired image in multi-sensor needs an image registration between two
images. Because it needs transformation from reference image to a coordinated system of sensed image. And this
represents each coordinated system independently between two images. Image registration use transformation of H
matrix. Method for overlay between two images uses blending based on HSV. Based on experimental results, the
proposed method shows high precision for fused pseudo image in multi-sensor, and can be represented image
registration by using probability-based method.