  Image restoration and enhancement is a process of improving the image quality
by removing degradations, such as noise, blur, and resolution degradation. Deep
learning (DL) has recently been applied to image restoration and enhancement.
Due to its ill-posed property, plenty of works have been explored priors to
facilitate training deep neural networks (DNNs). However, the importance of
priors has not been systematically studied and analyzed by far in the research
community. Therefore, this paper serves as the first study that provides a
comprehensive overview of recent advancements in priors for deep image
restoration and enhancement. Our work covers five primary contents: (1) A
theoretical analysis of priors for deep image restoration and enhancement; (2)
A hierarchical and structural taxonomy of priors commonly used in the DL-based
methods; (3) An insightful discussion on each prior regarding its principle,
potential, and applications; (4) A summary of crucial problems by highlighting
the potential future directions, especially adopting the large-scale foundation
models as prior, to spark more research in the community; (5) An open-source
repository that provides a taxonomy of all mentioned works and code links.
