Name
Title | ||
|---|---|---|
Two-scale decomposition-based multifocus image fusion framework combined with image morphology and fuzzy set theory |
Abstract | ||
|---|---|---|
Image fusion method can provide a high-quality image by merging the multiple features of different source images, and how to effectively evaluate the quality (informativeness) of image features is an important issue for image fusion. Because a considerable amount of imprecise and uncertain information exists in image fusion processes, this paper proposes a framework based on fuzzy set theory to handle the vague features, and a set of hybrid optimization methods is also designed to improve the performance. First, the two-scale decomposition method is utilized to decompose the source images and obtain a set of corresponding subimages. Second, fuzzy set theory and local spatial frequency are employed to generate preliminary decision maps by evaluating the pixel quality of the subimages. Third, a morphological method and consistency verification are utilized to optimize the decision maps to extract the focused and unfocused regions. Finally, three schemes are designed to generate the fused images according to the optimized decision maps. The experimental results show that the proposed method can achieve competitive performance compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2020.06.053 | Information Sciences |
Keywords | DocType | Volume |
Feature extraction,Fuzzy set theory,Image fusion,Image filter,Image morphology,Multisensor information fusion | Journal | 541 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Jiang | 1 | 8 | 3.53 |
| Xin Jin | 2 | 333 | 62.83 |
| Gao Chen | 3 | 53 | 4.78 |
| Shin-Jye Lee | 4 | 10 | 5.25 |
| Xiaohui Cui | 5 | 374 | 44.64 |
| Shaowen Yao | 6 | 86 | 26.85 |
| Liwen Wu | 7 | 0 | 2.70 |