Name
Abstract | ||
|---|---|---|
Multi-focus image fusion methods can be mainly divided into two categories: transform domain methods and spatial domain methods. Recent emerged deep learning (DL)-based methods actually satisfy this taxonomy as well. In this paper, we propose a novel DL-based multi-focus image fusion method that can combine the complementary advantages of transform domain methods and spatial domain methods. Specifically, a residual architecture that includes a multi-scale feature extraction module and a dual-attention module is designed as the basic unit of a deep convolutional network, which is firstly used to obtain an initial fused image from the source images. Then, the trained network is further employed to extract features from the initial fused image and the source images for a similarity comparison, aiming to detect the focus property of each source pixel. The final fused image is obtained by selecting corresponding pixels from the source images and the initial fused image according to the focus property map. Experimental results show that the proposed method can effectively preserve the original focus information from the source images and prevent visual artifacts around the boundary regions, leading to more competitive qualitative and quantitative performance when compared with the state-of-the-art fusion methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.inffus.2022.06.001 | Information Fusion |
Keywords | DocType | Volume |
Multi-focus image fusion,Transform domain methods,Spatial domain methods,Convolutional neural networks,Residual learning | Journal | 86-87 |
ISSN | Citations | PageRank |
1566-2535 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 492 | 30.80 |
| Lei Wang | 2 | 65 | 54.21 |
| Huafeng Li | 3 | 77 | 8.65 |
| Xun Chen | 4 | 458 | 52.73 |