An Exploration of Color Fusion with Multispectral Images for Night Vision Enhancement

Multispectral images usually present complimentary information such as visual-band imagery and infrared imagery (near infrared or long wave infrared). There are strong evidences that the fused multispectral imagery (in gray scales) increases the reliability of interpretation (Rogers & Wood, 1990; Essock et al., 2001) and thus good for machine analysis (computer vision); whereas the colorized multispectral imagery improves observer performance and reaction times (Toet et al. 1997; Varga, 1999; Waxman et al., 1996) and thus good for visual analysis (human vision). Imagine a nighttime navigation task that may be executed by an aircraft equipped with a multispectral imaging system. Analyzing the synthesized (fused or colorized) multisensory image will be more informative and more efficient than simultaneously monitoring multispectral images such as visual-band imagery (e.g., image intensified, II), near infrared (NIR) imagery, and infrared (IR) imagery, which may be displayed either on several split panels on a big screen or on several small screens. The focus of this chapter is how to synthesize a color presentation of multispectral images in order to enhance night vision. It is anticipated that the successful applications of night vision colorization techniques will lead to improved performance of remote sensing, nighttime navigation, target detection, and situational awareness. This colorization approaches mentioned here involve two main techniques, image fusion and colorization, which are briefly reviewed as follows, respectively. Image fusion combines multiple-source imagery by integrating complementary data in order to enhance the information apparent in the respective source images, as well as to increase the reliability of interpretation. This results in more accurate data (Keys et al., 1990) and increased utility (Rogers & Wood, 1990; Essock et al., 1999). In addition, it has been reported that fused data provides far more robust aspects of operational performance such as increased confidence, reduced ambiguity, improved reliability and improved classification (Rogers & Wood, 1990; Essock et al., 2001). A general framework of image fusion can be found in Reference (Pohl & Genderen, 1998). The discussions of image fusion here are limited to pixel-level fusion. Two commonly used fusion methods are the discrete wavelet transform (DWT) (Pu & Ni, 2000; Nunez et al., 1999) and various pyramids (such as Laplacian, contrast, gradient, and morphological pyramids) (Jahard et al., 1997; Ajazzi et al., 1998), which both are multiscale