{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:10:38Z","timestamp":1783401038442,"version":"3.54.6"},"reference-count":50,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,8]],"date-time":"2021-02-08T00:00:00Z","timestamp":1612742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602432 and 61401425"],"award-info":[{"award-number":["61602432 and 61401425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.<\/jats:p>","DOI":"10.3390\/s21041188","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"1188","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Infrared-Visible Image Registration Method Based on the Constrained Point Feature"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5130-9049","authenticated-orcid":false,"given":"Qingqing","family":"Li","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangliang","family":"Han","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peixun","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6027-1337","authenticated-orcid":false,"given":"Hang","family":"Yang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Wu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103823","DOI":"10.1016\/j.compbiomed.2020.103823","article-title":"Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation","volume":"123","author":"Wang","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Polinati, S., and Dhuli, R. (2019, January 4\u20136). A Review on Multi-Model Medical Image Fusion. Proceedings of the 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2019.8697906"},{"key":"ref_4","first-page":"715","article-title":"Spatial frequency discrete wavelet transform image fusion technique for remote sensing applications","volume":"22","author":"Jinju","year":"2019","journal-title":"Eng. Sci. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhou, H., and Gao, H. (2014). Fusion method for remote sensing image based on fuzzy integral. J. Electr. Comput. Eng.","DOI":"10.1155\/2014\/437939"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.infrared.2017.11.006","article-title":"Infrared and visible image fusion using discrete cosine transform and swarm intelligence for surveillance applications","volume":"88","author":"Paramanandham","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.patcog.2007.06.022","article-title":"Integrated multilevel image fusion and match score fusion of visible and infrared face images for robust face recognition","volume":"41","author":"Singh","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.inffus.2018.09.009","article-title":"Multimodal image registration using Laplacian commutators","volume":"49","author":"Zimmer","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107377","DOI":"10.1016\/j.patcog.2020.107377","article-title":"Non-rigid infrared and visible image registration by enhanced affine transformation","volume":"106","author":"Min","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sun, X., Xu, T., Zhang, J., and Li, X. (2017). A hierarchical framework combining motion and feature information for infrared-visible video registration. Sensors, 17.","DOI":"10.3390\/s17020384"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image matching from handcrafted to deep features: A survey","volume":"129","author":"Ma","year":"2020","journal-title":"Int. J. Comput. Vision"},{"key":"ref_12","unstructured":"Zhao, F., Huang, Q., and Gao, W. (2006, January 14\u201319). Image Matching by Normalized Cross-Correlation. Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, France."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/83.506761","article-title":"An FFT-based technique for translation, rotation, and scale-invariant image registration","volume":"5","author":"Reddy","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, Z., Kang, J., Yao, J., Song, W., and Tong, X. (2020). Robust fine registration of multisensor remote sensing images based on enhanced subpixel phase correlation. Sensors, 20.","DOI":"10.3390\/s20154338"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1109\/TMI.2003.815867","article-title":"Mutual-information-based registration of medical images: A survey","volume":"22","author":"Pluim","year":"2003","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1016\/j.patcog.2014.12.014","article-title":"Feature neighbourhood mutual information for multi-modal image registration: An application to eye fundus imaging","volume":"48","author":"Legg","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1109\/TIP.2017.2776753","article-title":"Normalized total gradient: A new measure for multispectral image registration","volume":"27","author":"Chen","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.optcom.2019.06.041","article-title":"Block-based multispectral image registration with application to spectral color measurement","volume":"451","author":"Shen","year":"2019","journal-title":"Opt. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.media.2019.03.006","article-title":"BIRNet: Brain image registration using dual-supervised fully convolutional networks","volume":"54","author":"Fan","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_20","first-page":"640","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2017.12.012","article-title":"A deep learning framework for remote sensing image registration","volume":"145","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106767","DOI":"10.1016\/j.compeleceng.2020.106767","article-title":"Medical image registration using deep neural networks: A comprehensive review","volume":"87","author":"Boveiri","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1016\/j.procs.2020.04.259","article-title":"Remote sensing image registration methodology: Review and discussion","volume":"171","author":"Tondewad","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.infrared.2019.04.021","article-title":"A grayscale weight with window algorithm for infrared and visible image registration","volume":"99","author":"Yu","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.cageo.2007.10.005","article-title":"A fast and fully automatic registration approach based on point features for multi-source remote-sensing images","volume":"34","author":"Yu","year":"2008","journal-title":"Comput. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103549","DOI":"10.1016\/j.infrared.2020.103549","article-title":"Registration of multimodal images with edge features and scale invariant PIIFD","volume":"111","author":"Chen","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"102825","DOI":"10.1016\/j.cviu.2019.102825","article-title":"Region-based image registration for remote sensing imagery","volume":"189","author":"Okorie","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_29","first-page":"10","article-title":"A combined corner and edge detector","volume":"15","author":"Harris","year":"1988","journal-title":"Alvey Vis. Conf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Misra, I., Moorthi, S.M., Dhar, D., and Ramakrishnan, R. (2012, January 15\u201317). An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model. Proceedings of the International Conference on Recent Advances in Information Technology, Dhanbad, India.","DOI":"10.1109\/RAIT.2012.6194482"},{"key":"ref_31","unstructured":"Pei, Y., Wu, H., Yu, J., and Cai, G. (2010, January 18\u201319). Effective Image Registration based on Improved Harris Corner Detection. Proceedings of the International Conference on Information, Networking and Automation (ICINA), Kunming, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.patrec.2016.09.011","article-title":"Enhancing sift-based image registration performance by building and selecting highly discriminating descriptors","volume":"84","author":"Lv","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Gool, L.V. (2006). SURF: Speeded up robust features. ECCV 2006\u2014European Conference on Computer Vision\u2014Volume Part I, Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.measurement.2015.01.011","article-title":"Adaptive registration algorithm of color images based on SURF","volume":"66","author":"Huang","year":"2015","journal-title":"Measurement"},{"key":"ref_37","unstructured":"Ke, Y., and Sukthankar, R. (July, January 27). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition, Washington, DC, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/LGRS.2016.2600858","article-title":"Remote sensing image registration with modified sift and enhanced feature matching","volume":"14","author":"Ma","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006). Machine Learning for High-Speed Corner Detection. ECCV 2006\u2014European Conference on Computer Vision, Springer.","DOI":"10.1007\/11744023_34"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.cviu.2011.10.006","article-title":"An iterative integrated framework for thermal\u2013visible image registration, sensor fusion, and people tracking for video surveillance applications","volume":"116","author":"Torabi","year":"2012","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"St-Charles, P.-L., Bilodeau, G.-A., and Bergevin, R. (2015, January 7\u201312). Online multimodal video registration based on shape matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301293"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_44","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, H., Wang, P., and Jing, L. (2020). An image registration method for multisource high-resolution remote sensing images for earthquake disaster assessment. Sensors, 20.","DOI":"10.3390\/s20082286"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2014.2325970","article-title":"A novel point-matching algorithm based on fast sample consensus for image registration","volume":"12","author":"Wu","year":"2015","journal-title":"Geosci. Remote Sens. Lett. IEEE"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3078","DOI":"10.1109\/TGRS.2018.2790483","article-title":"OS-sift: A robust sift-like algorithm for high-resolution optical-to-sar image registration in suburban areas","volume":"56","author":"Xiang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1188\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:21:20Z","timestamp":1760160080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,8]]},"references-count":50,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041188"],"URL":"https:\/\/doi.org\/10.3390\/s21041188","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,8]]}}}