摘要
Abstract
The natural color fusion of infrared images and visible images can significantly improve the situation perception and target detection of human vision. Sample-based color fusion is a fast, effective, and real-time natural color fusion algorithm. In view of the problems of the existing algorithm in terms of the color look-up table construction, i.e., sample data collision and look-up table incompleteness, this paper proposes a new natural color fusion algorithm based on the BP neural network. In the algorithm, the mapping f(g1, g2)→(R, G, B) between grayscale and color is obtained by using the BP neural network to nonlinearly fit between the two-dimensional grayscale vector (g1, g2) and the three-dimensional color vector (R, G, B) of the image simples. Subsequently, the color look-up table is constructed based on the mapping. During color fusing, the fused image is obtained by the color look-up table and the input grayscale g1,g2of dual-band images. The experiments show that the fused images based on the proposed algorithm have natural colors and are easily distinguishable objects. The fusion results obtained by the proposed algorithm are almost as good as or even better than the fusion results by Toet's method in terms of definition, colorfulness, and mapping accuracy.关键词
彩色融合/颜色查找表/色彩映射/BP神经网络/非线性拟合Key words
color fusion/color look-up table/color mapping/BP neural network/nonlinear fitting分类
信息技术与安全科学
