Multi-focus image fusion based on pulse coupled neural network and WSEML in DTCWT domain
The goal of multi-focus image fusion is to merge near-focus and far-focus images of the same scene to obtain an all-focus image that accurately and comprehensively represents the focus information of the entire scene. The current multi-focus fusion algorithms lead to issues such as the loss of detai...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1575606/full |
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| Summary: | The goal of multi-focus image fusion is to merge near-focus and far-focus images of the same scene to obtain an all-focus image that accurately and comprehensively represents the focus information of the entire scene. The current multi-focus fusion algorithms lead to issues such as the loss of details and edges, as well as local blurring in the resulting images. To solve these problems, a novel multi-focus image fusion method based on pulse coupled neural network (PCNN) and weighted sum of eight-neighborhood-based modified Laplacian (WSEML) in dual-tree complex wavelet transform (DTCWT) domain is proposed in this paper. The source images are decomposed by DTCWT into low- and high-frequency components, respectively; then the average gradient (AG) motivate PCNN-based fusion rule is used to process the low-frequency components, and the WSEML-based fusion rule is used to process the high-frequency components; we conducted simulation experiments on the public Lytro dataset, demonstrating the superiority of the algorithm we proposed. |
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| ISSN: | 2296-424X |