Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware
  Depth-from-Defocus
Zhuofeng Wu Yusuke Monno Masatoshi Okutomi
In this paper, we address the task of aberration-aware depth-from-defocus
(DfD), which takes account of spatially variant point spread functions (PSFs)
of a real camera. To effectively obtain the spatially variant PSFs of a real
camera without requiring any ground-truth PSFs, we propose a novel
self-supervised learning method that leverages the pair of real sharp and
blurred images, which can be easily captured by changing the aperture setting
of the camera. In our PSF estimation, we assume rotationally symmetric PSFs and
introduce the polar coordinate system to more accurately learn the PSF
estimation network. We also handle the focus breathing phenomenon that occurs
in real DfD situations. Experimental results on synthetic and real data
demonstrate the effectiveness of our method regarding both the PSF estimation
and the depth estimation.