  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.
