局部线性嵌入与模糊 C-均值聚类的红外图像彩色化算法

针对红外图像彩色化处理后清晰度低、色彩不够自然等问题，将改进后的局部线 性嵌入算法(LLE)算法引入到红外图像彩色化应用中，提出了一种 LLE 与模糊 C-均值聚类的红 外图像彩色化算法。首先通过扩大邻域范围和添加权重信息等方法改善了 LLE 算法敏感于稀疏 矩阵的缺陷，在红外和彩色模板像素矢量化空间中，利用最近邻搜索计算最佳匹配系数，经彩 色值计算将彩色模板中的颜色映射到红外图像特定区域，完成模板彩色与红外目标图像的颜色 传递。利用改进后的模糊 C-均值聚类对彩色化后的红外图像进行颜色聚类，在颜色聚类集上利 用直方图均衡化进行分段颜色均衡处理，最后将均衡化后的图像合成。将本算法与其他两种红 外彩色化算法进行仿真对比，实验结果表明，与其他两种算法相比，提出的红外图像彩色化算 法在仅仅利用目标红外图像和彩色模板下就能获得图像较为清晰、目标突出的彩色化结果。

An Infrared Image Colorization Algorithm Based on Local Linear Embedding and Fuzzy C- Means Clustering

To deal with the problems of low definition and unnatural color after infrared image colorization, this paper introduces an improved local linear embedding algorithm into the application of infrared image colorization, and proposes an infrared image colorization that features local linear embedding and fuzzy c-means clustering. Firstly, by enlarging the neighborhood range and adding weight information, the algorithm improves the defect of the local linear embedding algorithm which is sensitive to sparse matrix. In the space characterized by pixel vectorization of infrared and color template, Nearest Neighbor Search is used to figure out the best match coefficient. The color of color template can be mapped onto the specific areas on infrared image by computing color value, then color transfer from the template to the targeted infrared image done. The improved fizzy C-average clustering is carried out to conduct color clustering on infrared image, and on the color clustering set, histogram equalization to equalize segmented color, after which equalized image is finally synthesized. After the simulation comparison between this algorithm and the other two infrared colorization algorithms, the result shows that the proposed infrared image colorization algorithm can achieve clearer and target-prominent colorization only by target infrared images and color templates.