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In the fields of image restoration and image fusion, model-driven methods anddata-driven methods are the two representative frameworks. However, bothapproaches have their respective advantages and disadvantages. The model-drivenmethods consider the imaging mechanism, which is deterministic andtheoretically reasonable; however, they cannot easily model complicatednonlinear problems. The data-driven methods have a stronger prior knowledgelearning capability for huge data, especially for nonlinear statisticalfeatures; however, the interpretability of the networks is poor, and they areover-dependent on training data. In this paper, we systematically investigatethe coupling of model-driven and data-driven methods, which has rarely beenconsidered in the remote sensing image restoration and fusion communities. Weare the first to summarize the coupling approaches into the following threecategories: 1) data-driven and model-driven cascading methods; 2) variationalmodels with embedded learning; and 3) model-constrained network learningmethods. The typical existing and potential coupling methods for remote sensingimage restoration and fusion are introduced with application examples. Thispaper also gives some new insights into the potential future directions, interms of both methods and applications.

Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion