Multifocus image fusion using convolutional neural network
| Autor: | Xiaomin Yang, Marcelo Keese Albertini, Yu Wen, Turgay Celik, Olga S. Sushkova |
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| Rok vydání: | 2020 |
| Předmět: |
Image fusion
Computer Networks and Communications Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Convolutional neural network law.invention Lens (optics) Hardware and Architecture law 0202 electrical engineering electronic engineering information engineering Media Technology Computer vision Artificial intelligence business Software |
| Zdroj: | Multimedia Tools and Applications. 79:34531-34543 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | Acquiring all-in-focus images is significant in the multi-media era. Limited by the depth-of-field of the optical lens, it is hard to acquire an image with all targets are clear. One possible solution is to merge the information of a few complementary images in the same scene. In this article, we employ a two-channel convolutional network to derive the clarity map of source images. Then, the clarity map is smoothed by using morphological filtering. Finally, the fusion image is constructed via merging the clear parts of source images. Experimental results prove that our approach has a better performance on both visual quality and quantitative evaluations than many previous fusion approaches. |
| Databáze: | OpenAIRE |
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