Image Fusion Based on Color Transfer Technique

In 2001, with the nonlinear l┙┚ space (Ruderman et al., 1998), Reinhard et al. (Reinhard et al., 2001) introduced a method to transfer colors between two color images. The goal of their work was to make a synthetic image take on another image’s look and feel. Applying Reinhard’s statistical color transfer strategy, Toet (Toet, 2003) subsequently developed a color-transfer-based image fusion algorithm. With an appropriate daylight color image as the target image, the method can produce a natural appearing “daytime-like” color fused image and significantly improve observer performance. Therefore, the Toet’s approach has received considerable attention in recent years (Li & Wang, 2007b; Li et al., 2010a; Li et al., 2010b; Li et al., 2005; Tsagaris & Anastassopoulos, 2005; Toet & Hogervorst, 2008; Toet & Hogervorst, 2009; Wang et al., 2007; Zheng & Essock, 2008). Although Toet’s work implies that Reinhard’s l┙┚ color transfer method can be successfully applied to image fusion, it is difficult to develop a fast color image fusion algorithm based on this color transfer technique. The main reason is that it is restricted to the nonlinear l┙┚ space (See Appendix A). This color space is logarithmic, the transformation between RGB and l┙┚ spaces must be transmitted through LMS and logLMS spaces. This therefore increases the system’s storage requirements and computational time. On the other hand, since the dynamic range of the achromatic component in l┙┚ space is very different from that of a normal grayscale image, it becomes inconvenient to enhance the luminance contrast of the final color fused image in l┙┚ space by using conventional methods, such as directly using a high contrast grayscale fused image to replace the luminance component of the color fused image. To eliminate the limitations mentioned above, we (Li et al., 2010a) employed YCBCR space to implement Reinhard’s color transfer scheme and applied the YCBCR color transfer technique to the fusion of infrared and visible images. Through a series of mathematical derivation and proof, we (Li et al., 2010b) further presented a fast color-transfer-based image fusion algorithm. Our experiments demonstrate the performance of the fast color-transfer-based image fusion method is superior to other related color image fusion methods, including the Toet’s approach. The rest of this chapter is organized as follows. Section 2 reviews Reinhard’s l┙┚ color transfer method. Section 3 outlines our YCBCR color transfer method. Section 4 describes two basic image fusion methods based on the YCBCR color transfer technique. Section 5 introduces our fast color-transfer-based image fusion method. Experimental results and