  In the fields of image restoration and image fusion, model-driven methods and
data-driven methods are the two representative frameworks. However, both
approaches have their respective advantages and disadvantages. The model-driven
methods consider the imaging mechanism, which is deterministic and
theoretically reasonable; however, they cannot easily model complicated
nonlinear problems. The data-driven methods have a stronger prior knowledge
learning capability for huge data, especially for nonlinear statistical
features; however, the interpretability of the networks is poor, and they are
over-dependent on training data. In this paper, we systematically investigate
the coupling of model-driven and data-driven methods, which has rarely been
considered in the remote sensing image restoration and fusion communities. We
are the first to summarize the coupling approaches into the following three
categories: 1) data-driven and model-driven cascading methods; 2) variational
models with embedded learning; and 3) model-constrained network learning
methods. The typical existing and potential coupling methods for remote sensing
image restoration and fusion are introduced with application examples. This
paper also gives some new insights into the potential future directions, in
terms of both methods and applications.
