High performance imaging of large-scale dynamic scenes is substantial to vision intelligence. The light field is a 3D plenoptic function that describes the amount of light flow in every direction through every point in space. By recording the high dimensional light signal, the light field can accurately perceive the complex dynamic environment, supporting the understanding and decision-making of the intelligent system. Computational light field imaging technique, based on the light field and the representation of plenoptic function, aims to combine computation, digital sensors, optical system, and intelligent lighting, thereby combining the hardware design and software computing power. This technique breaks through the limits of classical imaging model and digital camera, establishes the relationship among light in spatial, angular, spectral, and temporal dimensions, realizes coupling perception, decoupling reconstruction, and intelligent processing, and leads to the multi-dimensional and multi-scale imaging ability for large-scale dynamic scenes. Light field imaging technique plays vital role in various fields, including life science, industrial inspection, national security, unmanned system, VR/AR, etc., attracting broad interests from both academia and industry. With the discrete sampling of high dimensional data, light field imaging faces the challenge of dimension trade-off between spatial resolution and angular resolution. How to reconstruct light field for sparse sampled data becomes a fundamental problem in computational light field imaging and its applications. Meanwhile, limited by high dimensional data perception of light field signals, light field process faces the contradiction between effective data perception and computational efficiency. How to replace the traditional two-dimensional imaging visual perception method with light field which is a high-dimensional information acquisition means and how to combine intelligent information processing technique to realize intelligent efficient perception, are huge challenges for industrial applications of the light field imaging technique. In this paper, we conduct a thorough literature review of devices and algorithms of computational light field imaging, including the representation and theory of light field, light field signal sampling, and light field reconstruction with super-resolution in spatial and angular domain.