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：点扩散函数工程是一种强大的计算成像技术，通过将定制相位掩模集成到光学系统中以编码额外信息到捕获图像中。结合深度学习使用时，此类系统现已在单目深度估计、扩展景深成像、无透镜成像及其他任务中达到最优性能。受空间光调制器技术最新进展的启发，本文回答了一个自然问题：随时间动态改变相位掩模是否能够编码更多信息并实现更优性能？我们首先证明静态相位掩模描述的点扩散函数集合是非凸的，因此由动态相位掩模生成的时间平均点扩散函数本质上更具表达能力。随后通过仿真实验证明，时间平均动态相位掩模能够显著提升单目深度估计与扩展景深成像的性能。