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- W4306809393 abstract "Multi-focus image fusion (MFIF) and super-resolution (SR) are the inverse problem of imaging model, purposes of MFIF and SR are obtaining all-in-focus and high-resolution 2D mapping of targets. Though various MFIF and SR methods have been designed; almost all the them deal with MFIF and SR separately. This paper unifies MFIF and SR problems in the physical perspective as the multi-focus image super resolution fusion (MFISRF), and we propose a novel unified dataset-free unsupervised framework named deep fusion prior (DFP) based-on deep image prior (DIP) to address such MFISRF with single model. Experiments have proved that our proposed DFP approaches or even outperforms those state-of-art MFIF and SR method combinations. To our best knowledge, our proposed work is a dataset-free unsupervised method to simultaneously implement the multi-focus fusion and super-resolution task for the first time. Additionally, DFP is a general framework, thus its networks and focus measurement tactics can be continuously updated to further improve the MFISRF performance. DFP codes are open source available at http://github.com/GuYuanjie/DeepFusionPrior." @default.
- W4306809393 created "2022-10-20" @default.
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- W4306809393 date "2021-10-11" @default.
- W4306809393 modified "2023-09-26" @default.
- W4306809393 title "Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging" @default.
- W4306809393 doi "https://doi.org/10.48550/arxiv.2110.05706" @default.
- W4306809393 hasPublicationYear "2021" @default.
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