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arXiv · 2110.05706

Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging

Yuanjie Gu·Yinghan Guan·Zhibo Xiao·Haoran Dai·Cheng Liu·Shouyu Wang
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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.

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Yuanjie Gu, Yinghan Guan, Zhibo Xiao, Haoran Dai, Cheng Liu, Shouyu Wang. 2021-10-12. Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging. https://doi.org/10.1117/1.oe.61.12.123103

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