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Structural Similarity Loss for Learning to Fuse Multi-Focus Images
Convolutional neural networks have recently been used for multi-focus image fusion. However, some existing methods have resorted to adding Gaussian blur to focused images, to simulate defocus, thereby generating data (with ground-truth) for supervised learning. Moreover, they classify pixels as ‘foc...
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| 出版年: | Sensors (Basel) |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7699701/ https://ncbi.nlm.nih.gov/pubmed/33233568 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s20226647 |
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