Image registration is a challenging problem for computer vision, and accurate and effective image registration is still required in various computer vision applications, e.g., 3-D scanning, autonomous navigation, and augmented reality. However, image registration becomes difficult due to the presence of noise and photometric changes. This paper presents a novel image registration method with unsupervised inverse intensity compensation (ICIR). This methodology uses weighted vectors to compensate for areas affected by radiometric variations. This is a 5-D vector body composed of RGB, brightness, and gradient, that is, each pixel is represented by a 5-D vector in its neighborhood. When performing image registration, the vector angle metric robust to illumination effect is used to calculate cost volumes. Then the selected cost metrics are aggregated based on RGB-Gradient tree structure. Experiments performed on stereo images of the Middlebury datasets and ours demonstrate this methodology in calculation accuracy and time all have good performance.