Multi-modal Image Fusion with KNN Matting
A single captured image of a scene is usually insufficient to reveal all the details due to the imaging limitations of single senor.To solve this problem,multiple images capturing the same scene with different sensors can be combined into a single fused image which preserves the complementary information of all input images.In this paper,a novel K nearest neighbor (KNN) matting based image fusion technique is proposed which consists of the following steps: First,the salient pixels of each input image is detected using a Laplician filtering based method.Then,guided by the salient pixels and the spatial correlation among adjacent pixels,the KNN matting method is used to calculate a globally optimal weight map for each input image.Finally,the fused image is obtained by calculating the weighed average of the input images.Experiments demonstrate that the proposed algorithm can generate high-quality fused images in terms of good visual quality and high objective indexes.Comparisons with a number of recently proposed fusion techniques show that the proposed method generates better results in most cases.
Image fusion KNN matting Laplician filtering weighted average
Xia Zhang Hui Lin Xudong Kang Shutao Li
Vision and Image Processing Labrataory,College of Electrical and Information Engineering,Hunan University,Changsha,China,410082
国际会议
Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)
长沙
英文
89-96
2014-11-01（万方平台首次上网日期，不代表论文的发表时间）