Name
Abstract | ||
|---|---|---|
Pixel-level image fusion has been investigated in various applications and a number of algorithms have been developed and proposed. However, few authors have addressed the problem of how to assess the performance of those algorithms and evaluate the resulting fused images objectively and quantitatively. In this study, two new fusion quality indexes are proposed and implemented through using the phase congruency measurement of the input images. Therefore, the feature-based measurements can provide a blind evaluation of the image fusion result, i.e. no reference image is needed. These metrics take the advantage of the phase congruency measurement which provides a dimensionless contrast- and brightness-invariant representation of image features. The fusion quality indexes are compared with recently developed blind evaluation metrics. The validity of the new metrics are identified by the test on the fusion results achieved by a number of multiresolution pixel-level fusion algorithms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.cviu.2007.04.003 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
fusion quality index,fusion result,pixel-level image fusion,new fusion quality index,input image,image quality,multiresolution pixel-level fusion algorithm,quantitative evaluation,image fusion result,feature measurement,image feature,cross-correlation,phase congruency,phase congruency measurement,image fusion,fused image,image features,cross correlation | Computer vision,Image fusion,Pattern recognition,Feature (computer vision),Multiresolution analysis,Fusion,Image quality,Sensor fusion,Artificial intelligence,Pixel,Phase congruency,Mathematics | Journal |
Volume | Issue | ISSN |
109 | 1 | Computer Vision and Image Understanding |
Citations | PageRank | References |
23 | 0.75 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Liu | 1 | 339 | 39.14 |
| David S. Forsyth | 2 | 43 | 3.54 |
| Robert Laganière | 3 | 300 | 35.20 |