Abstract
A quantum implementation of image registration is proposed based on a quantum version of Powell’s conjugate direction method in this study. Quantum Powell’s method can find the minimum parameters of similarity measurement between the quantum fixed and moving images in the solution space when the quantum moving image performs geometric transformation. By combining quantum computing units and basic quantum gates, a series of quantum circuits are designed to implement the quantum image registration method which contains quantum image similarity measure, quantum one-dimensional search algorithm, quantum updating search direction array and quantum termination condition of Powell’s iteration. To improve the search efficiency of the algorithm, a quantum version of golden section search method is proposed. The simulation experiments verify the validity of quantum Powell’s image registration method.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Data will be made available upon reasonable request.
References
Wang, Z., Xu, M., Zhang, Y.: Review of quantum image processing. Arch. Comput. Methods Eng. 29(2), 737–761 (2021). https://doi.org/10.1007/s11831-021-09599-2
Yan, F., Iliyasu, A.M., Le, P.Q.: Quantum image processing: A review of advances in its security technologies. Int. J. Quantum Inf. 15(03), 1730001 (2017). https://doi.org/10.1142/s0219749917300017
Grover, L.K.: Synthesis of quantum superpositions by quantum computation. Phys. Rev. Lett. 85(6), 1334–1337 (2000). https://doi.org/10.1103/physrevlett.85.1334
Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Mod. Phys. 81(2), 865–942 (2009). https://doi.org/10.1103/revmodphys.81.865
Guanlei, X., Xiaogang, X., Xun, W., Xiaotong, W.: A novel quantum image parallel searching algorithm. Optik 209, 164565 (2020). https://doi.org/10.1016/j.ijleo.2020.164565
Tezuka, H., Nakaji, K., Satoh, T., Yamamoto, N.: Grover search revisited: Application to image pattern matching. Phys. Rev. A 105(3), 032440 (2022). https://doi.org/10.1103/physreva.105.032440
Yan, F., Zhao, S., Venegas-Andraca, S.E., Hirota, K.: Implementing bilinear interpolation with quantum images. Digit. Signal Process. 117, 103149 (2021). https://doi.org/10.1016/j.dsp.2021.103149
Dong, H., Lu, D., Li, C.: A novel qutrit representation of quantum image. Quantum Inf. Process. (2022). https://doi.org/10.1007/s11128-022-03450-8
Jiang, N., Ji, Z., Wang, J., Lu, X., Zhou, R.: Quantum image histogram statistics. Int. J. Theor. Phys. 59(11), 3533–3548 (2020). https://doi.org/10.1007/s10773-020-04614-x
Chetia, R., Boruah, S.M.B., Sahu, P.P.: Quantum image edge detection using improved sobel mask based on NEQR. Quant Inf. Process. (2021). https://doi.org/10.1007/s11128-020-02944-7
Gao, Y., Xie, H., Zhang, J., Zhang, H.: A novel quantum image encryption technique based on improved controlled alternated quantum walks and hyperchaotic system. Phys. A Stat. Mech. Appl. (2022). https://doi.org/10.1016/j.physa.2022.127334
Chen, G., Song, X., Venegas-Andraca, S.E., El-Latif, A.A.A.: QIRHSI: novel quantum image representation based on hsi color space model. Quantum Inf. Process. 21(1), 1–31 (2022). https://doi.org/10.1007/s11128-021-03337-0
Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003). https://doi.org/10.1016/s0262-8856(03)00137-9
Yuan, S., Qing, X., Hang, B., Qu, H.: Quantum color image median filtering in the spatial domain: theory and experiment. Quantum Inf. Process. 21(9), 1–18 (2022). https://doi.org/10.1007/s11128-022-03660-0
Oliveira, F.P.M., Tavares, J.M.R.S.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2012). https://doi.org/10.1080/10255842.2012.670855
Matl, S., Brosig, R., Baust, M., Navab, N., Demirci, S.: Vascular image registration techniques: A living review. Med. Image Anal. 35, 1–17 (2017). https://doi.org/10.1016/j.media.2016.05.005
Schnabel, J.A., Heinrich, M.P., Papież, B.W., Brady, S.J.M.: Advances and challenges in deformable image registration: From image fusion to complex motion modelling. Med. Image Anal. 33, 145–148 (2016). https://doi.org/10.1016/j.media.2016.06.031
Song, X., Wang, H., Venegas-Andraca, S.E., Abd El-Latif, A.A.: Quantum video encryption based on qubit-planes controlled-XOR operations and improved logistic map. Phys. A Stat. Mech. Appl. 537, 122660 (2020). https://doi.org/10.1016/j.physa.2019.122660
Guryanov, F., Krylov, A.: Fast medical image registration using bidirectional empirical mode decomposition. Signal Process. Image Commun. 59, 12–17 (2017). https://doi.org/10.1016/j.image.2017.04.003
Yan, F., Venegas-Andraca, S.E., Hirota, K.: Toward implementing efficient image processing algorithms on quantum computers. Soft Comput. (2022). https://doi.org/10.1007/s00500-021-06669-2
Huizinga, W., Poot, D.H.J., Guyader, J.-M., Klaassen, R., Coolen, B.F., van Kranenburg, M., van Geuns, R.J.M., Uitterdijk, A., Polfliet, M., Vandemeulebroucke, J., Leemans, A., Niessen, W.J., Klein, S.: PCA-based groupwise image registration for quantitative MRI. Med. Image Anal. 29, 65–78 (2016). https://doi.org/10.1016/j.media.2015.12.004
Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: Fast predictive image registration—a deep learning approach. NeuroImage 158, 378–396 (2017). https://doi.org/10.1016/j.neuroimage.2017.07.008
Yu, D., Yang, F., Yang, C., Leng, C., Cao, J., Wang, Y., Tian, J.: Fast rotation-free feature-based image registration using improved n-SIFT and GMM-based parallel optimization. IEEE Trans. Biomed. Eng. 63(8), 1653–1664 (2016). https://doi.org/10.1109/tbme.2015.2465855
Jian, M., Liu, X., Luo, H., Lu, X., Yu, H., Dong, J.: Underwater image processing and analysis: A review. Signal Process. Image Commun. 91, 116088 (2021). https://doi.org/10.1016/j.image.2020.116088
Landry, G., Nijhuis, R., Dedes, G., Handrack, J., Thieke, C., Janssens, G., de Xivry, J.O., Reiner, M., Kamp, F., Wilkens, J.J., Paganelli, C., Riboldi, M., Baroni, G., Ganswindt, U., Belka, C., Parodi, K.: Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation. Med. Phys. 42(3), 1354–1366 (2015). https://doi.org/10.1118/1.4908223
Gupta, S., Gupta, P., Verma, V.S.: Study on anatomical and functional medical image registration methods. Neurocomputing 452, 534–548 (2021). https://doi.org/10.1016/j.neucom.2020.08.085
Chen, Y., He, F., Zeng, X., Li, H., Liang, Y.: The explosion operation of fireworks algorithm boosts the coral reef optimization for multimodal medical image registration. Eng. Appl. Artif. Intell. 102, 104252 (2021). https://doi.org/10.1016/j.engappai.2021.104252
Sengupta, D., Gupta, P., Biswas, A.: A survey on mutual information based medical image registration algorithms. Neurocomputing 486, 174–188 (2022). https://doi.org/10.1016/j.neucom.2021.11.023
Azam, M.A., Khan, K.B., Ahmad, M., Mazzara, M.: Multimodal medical image registration and fusion for quality enhancement. Comput. Mater. Contin. 68(1), 821–840 (2021). https://doi.org/10.32604/cmc.2021.016131
Bermejo, E., Chica, M., Damas, S., Salcedo-Sanz, S., Cordón, O.: Coral reef optimization with substrate layers for medical image registration. Swarm Evol. Comput. 42, 138–159 (2018). https://doi.org/10.1016/j.swevo.2018.03.003
Bierbrier, J., Gueziri, H.-E., Collins, D.L.: Estimating medical image registration error and confidence: A taxonomy and scoping review. Med. Image Anal. 81, 102531 (2022). https://doi.org/10.1016/j.media.2022.102531
Zachiu, C., de Senneville, B.D., Moonen, C.T.W., Raaymakers, B.W., Ries, M.: Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration. Phys. Med. Biol. 63(15), 155016 (2018). https://doi.org/10.1088/1361-6560/aad109
Tang, K., Li, Z., Tian, L., Wang, L., Zhu, Y.: ADMIR–affine and deformable medical image registration for drug-addicted brain images. IEEE Access 8, 70960–70968 (2020). https://doi.org/10.1109/access.2020.2986829
Alam, F., Rahman, S.U., Ullah, S., Gulati, K.: Medical image registration in image guided surgery: Issues, challenges and research opportunities. Biocybern. Biomed. Eng. 38(1), 71–89 (2018). https://doi.org/10.1016/j.bbe.2017.10.001
Blendowski, M., Hansen, L., Heinrich, M.P.: Weakly-supervised learning of multi-modal features for regularised iterative descent in 3d image registration. Med. Image Anal. 67, 101822 (2021). https://doi.org/10.1016/j.media.2020.101822
Saygili, G.: Predicting medical image registration error with block-matching using three orthogonal planes approach. Signal Image Video Process. 14(6), 1099–1106 (2020). https://doi.org/10.1007/s11760-020-01650-2
Chen, M., Carass, A., Jog, A., Lee, J., Roy, S., Prince, J.L.: Cross contrast multi-channel image registration using image synthesis for MR brain images. Med. Image Anal. 36, 2–14 (2017). https://doi.org/10.1016/j.media.2016.10.005
Heinrich, M.P., Simpson, I.J.A., Papież, B.W., Brady, S.M., Schnabel, J.A.: Deformable image registration by combining uncertainty estimates from supervoxel belief propagation. Med. Image Anal. 27, 57–71 (2016). https://doi.org/10.1016/j.media.2015.09.005
Li, L., Luo, Z., He, F., Sun, K., Yan, X.: An improved partial similitude method for dynamic characteristic of rotor systems based on Levenberg–Marquardt method. Mech. Syst. Signal Process. 165, 108405 (2022). https://doi.org/10.1016/j.ymssp.2021.108405
Klein, S., Pluim, J.P.W., Staring, M., Viergever, M.A.: Adaptive stochastic gradient descent optimisation for image registration. Int. J. Comput. Vis. 81(3), 227–239 (2008). https://doi.org/10.1007/s11263-008-0168-y
Blais, A., Girvin, S.M., Oliver, W.D.: Quantum information processing and quantum optics with circuit quantum electrodynamics. Nat. Phys. 16(3), 247–256 (2020). https://doi.org/10.1038/s41567-020-0806-z
Chen, K., Yan, F., Hirota, K., Zhao, J.: Quantum implementation of Powell’s conjugate direction method. J. Adv. Comput. Intell. Intell. Inf. 23(4), 726–734 (2019). https://doi.org/10.20965/jaciii.2019.p0726
Chang, Y.-C.: N-dimension golden section search: Its variants and limitations. In: 2009 2nd International Conference on Biomedical Engineering and Informatics, pp. 1–6 (2009). https://doi.org/10.1109/BMEI.2009.5304779
Zhang, Y., Lu, K., Gao, Y., Wang, M.: NEQR: a novel enhanced quantum representation of digital images. Quantum Inf. Process. 12(8), 2833–2860 (2013). https://doi.org/10.1007/s11128-013-0567-z
Schmidt-Kaler, F., Häffner, H., Riebe, M., Gulde, S., Lancaster, G.P.T., Deuschle, T., Becher, C., Roos, C.F., Eschner, J., Blatt, R.: Realization of the Cirac–Zoller controlled-NOT quantum gate. Nature 422(6930), 408–411 (2003). https://doi.org/10.1038/nature01494
Shepherd, D.J.: On the role of Hadamard gates in quantum circuits. Quantum Inf. Process. 5(3), 161–177 (2006). https://doi.org/10.1007/s11128-006-0023-4
Wang, J., Jiang, N., Wang, L.: Quantum image translation. Quantum Inf. Process. 14(5), 1589–1604 (2014). https://doi.org/10.1007/s11128-014-0843-6
Yan, F., Chen, K., Venegas-Andraca, S.E., Zhao, J.: Quantum image rotation by an arbitrary angle. Quantum Inf. Process. (2017). https://doi.org/10.1007/s11128-017-1733-5
Acknowledgements
This work is supported by the Natural Science Foundation of Jilin Province, China (Grant No. 20210101474JC).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, K., Ren, Z., Yan, F. et al. Quantum implementation of image registration. Quantum Inf Process 22, 97 (2023). https://doi.org/10.1007/s11128-023-03834-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11128-023-03834-4