Research On The Region Of Interest Extractioin Of Quantum Image And Quantum 3D Model

In recent years,quantum computation,with its powerful computing power,has attracted wide attention of the scientific community and attracted more and more researchers to devote themselves to the quantum computation research.Image has been more and more widely used in real life,and more and more research results have been achieved.In addition,quantum computer surpasses classical computer in performance,so researchers hope to use quantum computer to solve the problems related to image processing and promote the its development.Therefore,quantum image processing has broad prospects.The extraction of region of interest(Region of Interest,ROI)plays an important role in image processing.It plays a significant role in image compression and image processing efficiency.However,there is no corresponding algorithm in the field of quantum image processing.After investigating the region of interest of classical images,we finally choose Luminance Contrast(LC)algorithm,which is based on saliency detection,and improve it.Then we propose a quantum image ROI extraction algorithm which is based on LC.The algorithm not only makes up for the blank of quantum image ROI extraction,but also has obvious advantages over the classical algorithm.The complexity analysis shows that the efficiency of the algorithm is much better than the classical algorithm and guarantees its effect.The experiment based on MSRA10 K saliency detection database proves that the accuracy of 90% images are more than 95%.Quantum image representation model is one of the two key problems which need to be solved in quantum image processing.Current research results are mainly concentrated in the field of two-dimensional images,and the representation methods of three-dimensional and multi-dimensional images are few and inefficient.In the paper,a new quantum three-dimensional model representation model which is called quantum point cloud is proposed based on the classical point cloud.In order to further improve the efficiency of the algorithm and save quantum resources,we use the idea of DPCM to compress the point cloud model and form a new quantum point cloud model.On the basis of this model,two simple quantum geometric transformation algorithms are proposed,which show the availability of the model in quantum image processing algorithms.