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, thereby paving the way for miniaturizing imaging systems. Based on computational imaging, the simple and compact optical imaging techniques are developed, which is also called simple optics. To develop miniaturized optical imaging elements and integrated systems, simple optics utilizes the joint design of optical system and image processing algorithms, thereby realizing high-quality imaging that is comparable to complex optical systems. The imaging systems are of small-size, low-weight, and low-power consumption. With the development 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. Owing to the introduction of encoding process and decoding process, an optical imaging model is developed to represent the relationship between the target object and the acquired signal, from which the computational reconstruction is used to restore the image. In the image restoration part, the algorithms are discussed in three categories, i.e. 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. In this review, the imaging techniques realized by simple optics are also discussed, 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 developing consumer electronics, unmanned driving, machine vision, security monitoring, biomedical devices and metaverse. Last but not least, the challenges and future developments are prospected.