  Color-coded aperture (CCA) methods can physically measure the depth of a
scene given by physical cues from a single-shot image of a monocular camera.
However, they are vulnerable to actual lens aberrations in real scenes because
they assume an ideal lens for simplifying algorithms. In this paper, we propose
physical cue-based deep learning for CCA photography. To address actual lens
aberrations, we developed a deep deaberration network (DDN) that is
additionally equipped with a self-attention mechanism of position and color
channels to efficiently learn the lens aberration. Furthermore, a new Bayes L1
loss function based on Bayesian deep learning enables to handle the uncertainty
of depth estimation more accurately. Quantitative and qualitative comparisons
demonstrate that our method is superior to conventional methods including real
outdoor scenes. Furthermore, compared to a long-baseline stereo camera, the
proposed method provides an error-free depth map at close range, as there is no
blind spot between the left and right cameras.
