In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state-of-the-art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP-hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors’ approach on the D-Wave quantum computer. The authors 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 optimisation problems.

中文翻译：

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量子退火合成孔径雷达图像分割

在图像处理中，图像分割是将数字图像分割成多个图像片段的过程。在最先进的方法中，马尔可夫随机场可用于对像素之间的依赖性进行建模，并通过最小化相关成本函数来实现分割。目前，为建模为马尔可夫随机场的给定图像找到最佳片段集似乎是 NP 困难的。作者的目标是利用量子计算的指数可扩展性来加速合成孔径雷达图像的分割。为此，作者提出了一种混合量子退火经典优化期望最大化算法来获得最佳片段集。在提出合适的公式后，作者讨论了作者方法在 D-Wave 量子计算机上的性能和可扩展性。作者还提出了对最优计算参数的简短研究，以揭示绝热量子计算解决组合优化问题的大型实例的局限性和潜力。