  Nowadays, image-to-image translation methods, are the state of the art for
the enhancement of natural images. Even if they usually show high performance
in terms of accuracy, they often suffer from several limitations such as the
generation of artifacts and the scalability to high resolutions. Moreover,
their main drawback is the completely black-box approach that does not allow to
provide the final user with any insight about the enhancement processes
applied. In this paper we present a path planning algorithm which provides a
step-by-step explanation of the output produced by state of the art enhancement
methods, overcoming black-box limitation. This algorithm, called eXIE, uses a
variant of the A* algorithm to emulate the enhancement process of another
method through the application of an equivalent sequence of enhancing
operators. We applied eXIE to explain the output of several state-of-the-art
models trained on the Five-K dataset, obtaining sequences of enhancing
operators able to produce very similar results in terms of performance and
overcoming the huge limitation of poor interpretability of the best performing
algorithms.
