The current state of traditional optoelectronic imaging technology is constrained by the inherent limitations of its hardware. These limitations pose significant challenges in acquiring higher-dimensional information and reconstructing accurate images, particularly in applications such as scattering imaging, super-resolution, and complex scene reconstruction. However, the rapid development and widespread adoption of deep learning are reshaping the field of optical imaging through computational imaging technology. Data-driven computational imaging has ushered in a paradigm shift by leveraging the nonlinear expression and feature learning capabilities of neural networks. This approach transcends the limitations of conventional physical models, enabling the adaptive extraction of critical features directly from data. As a result, computational imaging overcomes the traditional "what you see is what you get" paradigm, paving the way for more compact optical system designs, broader information acquisition, and improved image reconstruction accuracy. These advancements have significantly enhanced the interpretation of high-dimensional light-field information and the processing of complex images. This paper presents a comprehensive analysis of the integration of deep learning and computational imaging, emphasizing its transformative potential in three core areas: computational optical system design, high-dimensional information interpretation, and image enhancement and processing. Additionally, it addresses the challenges and future directions of this cutting-edge technology, providing novel insights into interdisciplinary imaging research.