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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3018261
|2 doi
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|a pubmed24n1308.xml
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|a (DE-627)NLM31419150X
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|a (NLM)32845840
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Ma, Haoyu
|e verfasserin
|4 aut
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| 245 |
1 |
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|a An α-Matte Boundary Defocus Model-Based Cascaded Network for Multi-focus Image Fusion
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| 264 |
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|c 2020
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Capturing an all-in-focus image with a single camera is difficult since the depth of field of the camera is usually limited. An alternative method to obtain the all-in-focus image is to fuse several images that are focused at different depths. However, existing multi-focus image fusion methods cannot obtain clear results for areas near the focused/defocused boundary (FDB). In this paper, a novel α-matte boundary defocus model is proposed to generate realistic training data with the defocus spread effect precisely modeled, especially for areas near the FDB. Based on this α-matte defocus model and the generated data, a cascaded boundary-aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB. Specifically, the MMF-Net consists of two cascaded subnets for initial fusion and boundary fusion. These two subnets are designed to first obtain a guidance map of FDB and then refine the fusion near the FDB. Experiments demonstrate that with the help of the new α-matte boundary defocus model, the proposed MMF-Net outperforms the state-of-the-art methods both qualitatively and quantitatively
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Liao, Qingmin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Juncheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Shaojun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xue, Jing-Hao
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2020) vom: 26. Aug.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:PP
|g year:2020
|g day:26
|g month:08
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2020.3018261
|3 Volltext
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|d PP
|j 2020
|b 26
|c 08
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