Point-spread-function (PSF) engineering is a powerful computational imaging techniques wherein a custom phase mask is integrated into an optical system to encode additional information into captured images. Used in combination with deep learning, such systems now offer state-of-the-art performance at monocular depth estimation, extended depth-of-field imaging, lensless imaging, and other tasks. Inspired by recent advances in spatial light modulator (SLM) technology, this paper answers a natural question: Can one encode additional information and achieve superior performance by changing a phase mask dynamically over time? We first prove that the set of PSFs described by static phase masks is non-convex and that, as a result, time-averaged PSFs generated by dynamic phase masks are fundamentally more expressive. We then demonstrate, in simulation, that time-averaged dynamic (TiDy) phase masks can offer substantially improved monocular depth estimation and extended depth-of-field imaging performance.

翻译：点扩散函数（PSF）工程是一种强大的计算成像技术，其中将自定义相位掩模集成到光学系统中，以将附加信息编码到捕获的图像中。与深度学习结合使用，这些系统现在提供在单眼深度估计、扩展的景深成像、无镜头成像和其他任务方面的最先进性能。本文受到最近空间光调制器技术的启发，回答了一个自然问题：通过随时间动态地改变相位掩模，能否编码附加信息并实现更高性能？我们首先证明，静态相位掩模描述的PSF集合是非凸的，因此，由动态相位掩模生成的时间平均PSF在基本上更具表达能力。然后，在模拟中演示了时间平均动态（TiDy）相位掩模可以提供显著改进的单眼深度估计和扩展景深成像性能。