  Multi-focus image fusion, a technique to generate an all-in-focus image from
two or more partially-focused source images, can benefit many computer vision
tasks. However, currently there is no large and realistic dataset to perform
convincing evaluation and comparison of algorithms in multi-focus image fusion.
Moreover, it is difficult to train a deep neural network for multi-focus image
fusion without a suitable dataset. In this letter, we introduce a large and
realistic multi-focus dataset called Real-MFF, which contains 710 pairs of
source images with corresponding ground truth images. The dataset is generated
by light field images, and both the source images and the ground truth images
are realistic. To serve as both a well-established benchmark for existing
multi-focus image fusion algorithms and an appropriate training dataset for
future development of deep-learning-based methods, the dataset contains a
variety of scenes, including buildings, plants, humans, shopping malls, squares
and so on. We also evaluate 10 typical multi-focus algorithms on this dataset
for the purpose of illustration.
