Name
Abstract | ||
|---|---|---|
For remote sensing image fusion, infrared and visible images have very different brightness due to their disparate imaging mechanisms, the result of which is that nontarget regions in the infrared image often affect the fusion of details in the visible image. This letter proposes a novel infrared and visible image fusion method basing hybrid representation learning by combining dictionary-learning-based joint sparse representation (JSR) and nonnegative sparse representation (NNSR). In the proposed method, different fusion strategies are adopted, respectively, for the mean image, which represents the primary energy information, and for the deaveraged image, which contains important detail features. Since the deaveraged image contains a large amount of high-frequency details information of the source image, JSR is utilized to sparsely and accurately extract the common and innovation features of the deaveraged image, thus, accurately merging high-frequency details in the deaveraged image. Then, the mean image represents low-frequency and overview features of the source image, according to NNSR, mean image is classified well-directed to different feature regions and then fused, respectively. Such proposed method, on the one hand, can eliminate the impact on fusion result suffering from very different brightness causing by different imaging mechanism between infrared and visible image; on the other hand, it can improve the readability and accuracy of the result fusion image. Experimental result shows that, compared with the classical and state-of-the-art fusion methods, the proposed method not only can accurately integrate the infrared target but also has rich background details of the visible image, and the fusion effect is superior. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/lgrs.2019.2907721 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Image fusion,Feature extraction,Silicon,Dictionaries,Brightness,Imaging,Remote sensing | Computer vision,Image fusion,Sparse approximation,Fusion,Feature extraction,Artificial intelligence,Merge (version control),Infrared,Brightness,Mathematics,Feature learning | Journal |
Volume | Issue | ISSN |
16 | 11 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiqing He | 1 | 1 | 2.38 |
| Jiaqi Ji | 2 | 1 | 2.38 |
| Dandan Dong | 3 | 0 | 1.69 |
| Jun Wang | 4 | 3 | 3.42 |
| Jianping Fan | 5 | 2677 | 192.33 |