An objective evaluation metric for color image fusion

Image fusion has been extensively studied in past two decades. By image fusion algorithms, a composite image (i.e., fused image) can be formed with several images from different sensors. The performance of image fusion methods can be assessed using subjective and/or objective measures. However, subjective evaluation involves human subjects, which significantly increases the cost of time and resource. In this paper, we will discuss objective evaluations of color image fusion algorithms. Given a reference color image and fused color images, we first convert the images into CIELab color space. Then we define four image metrics in CIELab space: the phase congruency metric (PCM), the image gradient magnitude metric (IGMM), the image contrast metric (ICM), and the color natural metric (CNM). Finally, with the four metrics, we propose an objective evaluation index (OEI) for a fused image to measure its similarity with the reference image. The larger the OEI value of a fused image is, the more similar the fused image is with the reference image. To validate the proposed metric, first the fused images are formed with different color fusion algorithms using a set of multispectral images (including visible color images, near infrared images, and long wave infrared images); and then the OEIs of fused images are calculated and compared. Experimental results show that the proposed objective evaluation index is very promising and fits well to subjective evaluation.