Implementation and analysis of quantum-classical hybrid interactive image segmentation algorithm based on quantum annealer

With the development of computer vision and digital image processing technology, image segmentation has become an important part of various image processing and image analysis. Since interactive segmentation can obtain more accurate results than automatic segmentation, the most representative Graph Cuts has gradually become a popular method in image segmentation. However, this algorithm has two significant disadvantages. On the one hand, if the background is complex or very similar to the foreground, the accuracy will be low; on the other hand, the algorithm is slow and the iteration process is complicated. To improve it, this paper proposes a new image segmentation algorithm based on quantum annealing and Graph Cuts. The algorithm beds the classical interactive image segmentation problem into a quantum optimization algorithm and obtains ideal image segmentation results on the D-Wave quantum annealer. Meanwhile, it is compared with the other three methods. Compared with MATLAB, the segmentation results are more beautiful, with an average precision higher than 5.27% and an average recall higher than 5.43%; the quantum annealing time is always lower than the simulated annealing time; and the success probability is more than twice that of the quantum approximate optimization algorithm. Therefore, it is concluded that this method is superior.