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.

翻译：本文研究了像差感知的散焦深度估计（DfD）任务，该任务考虑了真实相机中空间变化的点扩散函数（PSF）。为有效获取真实相机的空间变化PSF而无需任何真实PSF标注，我们提出了一种新颖的自监督学习方法，该方法利用通过改变相机光圈设置轻松捕获的真实清晰与模糊图像对。在PSF估计中，我们假设PSF具有旋转对称性，并引入极坐标系以更精确地学习PSF估计网络。我们还处理了真实DfD场景中出现的呼吸对焦现象。在合成与真实数据上的实验结果表明，我们的方法在PSF估计和深度估计方面均具有有效性。