  In image processing, image segmentation is the process of partitioning a
digital image into multiple image segment. Among state-of-the-art methods,
Markov Random Fields (MRF) can be used to model dependencies between pixels,
and achieve a segmentation by minimizing an associated cost function.
Currently, finding the optimal set of segments for a given image modeled as a
MRF appears to be NP-hard. In this paper, we aim to take advantage of the
exponential scalability of quantum computing to speed up the segmentation of
Synthetic Aperture Radar images. For that purpose, we propose an hybrid quantum
annealing classical optimization Expectation Maximization algorithm to obtain
optimal sets of segments. After proposing suitable formulations, we discuss the
performances and the scalability of our approach on the D-Wave quantum
computer. We also propose a short study of optimal computation parameters to
enlighten the limits and potential of the adiabatic quantum computation to
solve large instances of combinatorial optimization problems.
