  Multi-focus image fusion (MFF) is a fundamental task in the field of
computational photography. Current methods have achieved significant
performance improvement. It is found that current methods are evaluated on
simulated image sets or Lytro dataset. Recently, a growing number of
researchers pay attention to defocus spread effect, a phenomenon of real-world
multi-focus images. Nonetheless, defocus spread effect is not obvious in
simulated or Lytro datasets, where popular methods perform very similar. To
compare their performance on images with defocus spread effect, this paper
constructs a new dataset called MFF in the wild (MFFW). It contains 19 pairs of
multi-focus images collected on the Internet. We register all pairs of source
images, and provide focus maps and reference images for part of pairs. Compared
with Lytro dataset, images in MFFW significantly suffer from defocus spread
effect. In addition, the scenes of MFFW are more complex. The experiments
demonstrate that most state-of-the-art methods on MFFW dataset cannot robustly
generate satisfactory fusion images. MFFW can be a new baseline dataset to test
whether an MMF algorithm is able to deal with defocus spread effect.
