Imaging is a longstanding research topic in optics and photonics and is an important tool for a wide range of scientific and engineering fields. Computational imaging is a powerful framework for designing innovative imaging systems by incorporating signal processing into optics. Conventional approaches involve individually designed optical and signal processing systems, which unnecessarily increased costs. Computational imaging, on the other hand, enhances the imaging performance of optical systems, visualizes invisible targets, and minimizes optical hardware. Digital holography and computer-generated holography are the roots of this field. Recent advances in information science, such as deep learning, and increasing computational power have rapidly driven computational imaging and have resulted in the reinvention these imaging technologies. In this paper, I survey recent research topics in computational imaging, where optical randomness is key. Imaging through scattering media, non-interferometric quantitative phase imaging, and real-time computer-generated holography are representative examples. These recent optical sensing and control technologies will serve as the foundations of next-generation imaging systems in various fields, such as biomedicine, security, and astronomy.

成像是光学和光子学领域的一个长期研究课题，是广泛科学和工程领域的重要工具。计算成像是一个强大的框架，用于通过将信号处理融入光学来设计创新成像系统。传统方法涉及单独设计的光学和信号处理系统，这不必要地增加了成本。另一方面，计算成像增强了光学系统的成像性能，使不可见目标可视化，并最大限度地减少光学硬件。数字全息术和计算机生成全息术是该领域的根源。信息科学的最新进展，例如深度学习和计算能力的提高，迅速推动了计算成像的发展，并导致了这些成像技术的重新发明。在本文中，我调查了计算成像领域的最新研究主题，其中光学随机性是关键。通过散射介质成像、非干涉定量相位成像和实时计算机生成的全息术是代表性的例子。这些最新的光学传感和控制技术将成为生物医学、安全和天文学等各个领域的下一代成像系统的基础。