Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-45643-9_10 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Image fusion,KNN matting,Laplician filtering,weighted average | k-nearest neighbors algorithm,Spatial correlation,Image fusion,Pattern recognition,Computer science,Filter (signal processing),Fusion,Artificial intelligence,Pixel,Modal,Salient | Conference |
Volume | ISSN | Citations |
484 | 1865-0929 | 2 |
PageRank | References | Authors |
0.36 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xia Zhang | 1 | 2 | 0.36 |
| Hui Lin | 2 | 36 | 2.36 |
| Xudong Kang | 3 | 451 | 22.68 |
| Shutao Li | 4 | 2594 | 139.10 |