Name
Abstract | ||
|---|---|---|
Multi-focus image fusion technique is able to help obtaining an all-focused image, which is advantage to human vision and image processing. In this paper, a novel multi-focus image fusion method is proposed based on random walk and guided filter. In the proposed method, the decomposition function and the optimizing function of random walk are used in multi-focus image fusion. And the random walk is also utilized for weight maps directly. The advantages of random walk and guided filter in image fusion are fully utilized by regulating proportional coefficients artificially. The proposed method concludes six steps: first, decomposing source images into detail layers and base layers with random walk; second, the random walk is used for weight maps directly and the guided filter is used as smoothing filters to get the streamlined weight maps of the detail layers and the base layers, respectively; third, the weight maps of the detail layers and the base layers are acquired by summing the initializing weight maps in different proportions; and then, the final weight maps of the detail layers are acquired using random walk for optimizing. After that, the fused detail layer and base layer are obtained by weighted average of detail layers and base layers, singly. Finally, the fused image is gained by summing up the fused base layer and the fused detail layer. Experiments demonstrate that the proposed method outperforms many other approaches in both subjective and objective assessments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00530-019-00608-w | Multimedia Systems |
Keywords | Field | DocType |
Image fusion, Multi-focus image, Random walk, Guided filter, Weighted averaging | Image fusion,Computer science,Random walk,Algorithm,Image processing,Real-time computing,Multi focus,Smoothing,Initialization | Journal |
Volume | Issue | ISSN |
25 | 4 | 0942-4962 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaobin Wang | 1 | 14 | 2.62 |
| Lina Chen | 2 | 2 | 3.07 |
| Jian Li | 3 | 162 | 44.60 |
| Ying Zhu | 4 | 8 | 2.15 |