Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus
This addresses the challenge of accurate depth estimation in real-world camera systems by accounting for optical aberrations, but it is incremental as it builds on existing depth-from-defocus techniques with a novel self-supervised approach.
The paper tackles the problem of estimating spatially variant point spread functions (PSFs) for real cameras in depth-from-defocus, proposing a self-supervised learning method that uses pairs of sharp and blurred images without ground-truth PSFs, and demonstrates effectiveness in both PSF and depth estimation on synthetic and real data.
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.