Polarization image fast fusion method based on online dictionary learning
| Autor: | Guo-ming Xu, Guang-lin Yuan, Pu-cheng Zhou, Mogen Xue |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Image fusion
K-SVD Source data Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Histogram matching Pattern recognition Color space Associative array Matrix decomposition Computer Science::Computer Vision and Pattern Recognition RGB color model Computer vision Artificial intelligence business |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| Popis: | In this paper we propose a polarization image fast fusion approach based on online dictionary learning for sparse non-negative matrix factorization, aiming at improving the efficiency of fusion methods for polarization image based on non-negative matrix factorization. Firstly, all of the polarization parameter images are taken as source data sets for sparse non-negative matrix factorization using online dictionary learning algorithm, so as to extract three feature basis images. Then, after histogram matching, the three feature basis images are mapped into three color channels of IHS color space. Finally, the fused image is achieved via the transform from IHS to RGB color model. Experimental results show that, the proposed method not only has better capacity of color representation capability and effectively pop out detailed information of objects but enhances the running efficiency evidently as well. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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