Research advances in simple and compact optical imaging techniques

Abstract

Computational imaging enables optical imaging systems to acquire more information with miniaturized setups. Computational imaging can avoid the object-image conjugate limitation of the imaging system, and introduce encoding and decoding processes based on physical optics to achieve more efficient information transmission. It can simultaneously increase the amount of information and reduce the complexity of the system, paving the way for miniaturized imaging systems. Based on computational imaging, the simple optical imaging techniques are developed, which is also called simple optics. To develop miniaturized optical imaging elements and integrated systems, simple optics employs the joint design of optical system and image processing algorithms, realizing high-quality imaging that is comparable to complex optical systems. The imaging systems are small-size, low-weight, and consume low-power. With the developments of micro-nano manufacturing, the optical elements have evolved from a single lens or a few lenses, to flat/planar optical elements, such as diffractive optical elements, and metasurface optical elements. As a result, various lensless and metalens imaging systems have emerged. Due to the introduction of encoding and decoding processes, an optical imaging model is developed to represent the relationship between the target object and the acquired signal, from which computational reconstruction is used to restore the image. In the image restoration part, the algorithms are discussed in three categories, the classic algorithm, the model-based optimization iterative algorithm and the deep learning (neural network) algorithm. Besides, the end-to-end optimization is highlighted because it introduces a new frame to minimize the complexity of optical system. This review also discusses the imaging techniques realized by simple optics, such as depth imaging, high-resolution and super-resolution imaging, large field of view imaging, and extended depth of field imaging, as well as their important roles in the development of consumer electronics, unmanned driving, machine vision, security monitoring, biomedical devices and metaverse. Last but not least, the challenges and future developments are prospected.