  All-in-Focus (AIF) photography is expected to be a commercial selling point
for modern smartphones. Standard AIF synthesis requires manual, time-consuming
operations such as focal stack compositing, which is unfriendly to ordinary
people. To achieve point-and-shoot AIF photography with a smartphone, we expect
that an AIF photo can be generated from one shot of the scene, instead of from
multiple photos captured by the same camera. Benefiting from the multi-camera
module in modern smartphones, we introduce a new task of AIF synthesis from
main (wide) and ultra-wide cameras. The goal is to recover sharp details from
defocused regions in the main-camera photo with the help of the
ultra-wide-camera one. The camera setting poses new challenges such as
parallax-induced occlusions and inconsistent color between cameras. To overcome
the challenges, we introduce a predict-and-refine network to mitigate
occlusions and propose dynamic frequency-domain alignment for color correction.
To enable effective training and evaluation, we also build an AIF dataset with
2686 unique scenes. Each scene includes two photos captured by the main camera,
one photo captured by the ultrawide camera, and a synthesized AIF photo.
Results show that our solution, termed EasyAIF, can produce high-quality AIF
photos and outperforms strong baselines quantitatively and qualitatively. For
the first time, we demonstrate point-and-shoot AIF photo synthesis successfully
from main and ultra-wide cameras.
