• Infrared Technology
  • Vol. 44, Issue 7, 641 (2022)
Yunhong LI*, Yudong LIU, Xueping SU, Xuemin LUO, and Lan YAO
Author Affiliations
  • [in Chinese]
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    DOI: Cite this Article
    LI Yunhong, LIU Yudong, SU Xueping, LUO Xuemin, YAO Lan. Review of Infrared and Visible Image Registration[J]. Infrared Technology, 2022, 44(7): 641 Copy Citation Text show less
    References

    [1] LI Y, YU F Y, CAI Q, et al. Image fusion of fault detection in power system based on deep learning[J]. Cluster Computing the Journal of Networks Software Tools and Applications, 2019, 22(4): 9435-9443.

    [2] MA J, ZHAO J, MA Y, et al. Non-rigid visible and infrared face registration via regularized Gaussian fields criterion[J]. Pattern Recognition, 2015, 48(3): 772-784.

    [4] Sarvaiya J N, Patnaik S, Bombaywala S. Image registration by template matching using normalized cross-correlation[C]//2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies. IEEE, 2009: 819-822.

    [5] YANG Z, SHEN G, WANG W, et al. Spatial-spectral cross correlation for reliable multispectral image registration[C]//2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), 2009: 1-8.

    [6] MA J, JIANG X, FAN A, et al. Image matching from handcrafted to deep features: A survey[J]. International Journal of Computer Vision, 2021, 129(1): 23-79.

    [7] Stone H S, Tao B, McGuire M. Analysis of image registration noise due to rotationally dependent aliasing[J]. Journal of Visual Communication and Image Representation, 2003, 14(2): 114-135.

    [8] LI Z, YANG J, LI M, et al. Estimation of large scalings in images based on multilayer pseudopolar fractional Fourier transform[J]. Mathematical Problems in Engineering, 2013, 2013: 179489.

    [9] DONG Y, JIAO W, LONG T, et al. An extension of phase correlationbased image registration to estimate similarity transform using multiple polar Fourier transform[J]. Remote Sensing, 2018, 10(11): 1719.

    [10] Fujisawa T, Ikehara M. High-accuracy image rotation and scale estimation using radon transform and sub-pixel shift estimation[J]. IEEE Access, 2019, 7: 22719-22728.

    [11] Fouda Y, Ragab K. An efficient implementation of normalized crosscorrelation image matching based on pyramid[C]//2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing,IEEE, 2013: 98-103.

    [12] YANG M Y, QIANG Y, Rosenhahn B. A global-to-local framework for infrared and visible image sequence registration[C]//2015 IEEE Winter Conference on Applications of Computer Vision, 2015: 381-388.

    [13] BAI L F, HAN J, ZHANG Y, et al. Registration algorithm of infrared and visible images based on improved gradient normalized mutual information and particle swarm optimization[J]. Infrared Laser Engineering, 2012, 41(1): 248-254.

    [14] CHEN S J, SHEN H L, LI C, et al. Normalized total gradient: A new measure for multispectral image registration[J]. IEEE Transactions on Image Processing, 2017, 27(3): 1297-1310.

    [15] YANG T J, TANG Q, LI L. Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information[J]. Journal of Applied Clinical Medical Physics, 2019, 20(6): 99-110.

    [20] ZHUANG Y, GAO K, MIU X, et al. Infrared and visual image registration based on mutual information with a combined particle swarm optimization-Powell search algorithm]〕]. Optik, 2016, 127(1): 188-191.

    [21] LI Y, WANG J, YAO K. Modified phase correlation algorithm for image registration based on pyramid[J]. Alexandria Engineering Journal, 2022, 61(1): 709-718.

    [22] Morel J M, YU G. ASIFT: A new framework for fully affine invariant image comparison[J]. SIAM Journal on Imaging Sciences, 2009, 2(2): 438-469.

    [24] ZENG Q, ADU J, LIU J, et al. Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT[J]. Journal of Real-Time Image Processing, 2020, 17(5): 1103-1115.

    [25] JIANG Q, LIU Y, YAN Y, et al. A contour angle orientation for power equipment infrared and visible image registration[J]. IEEE Transactions on Power Delivery, 2020, 36(4): 2559-2569.

    [27] CHEN Y, DAI J, MAO X, et al. Image registration betwe en visible and infrared images for electrical equipment inspection robots based on quadrilateral features[C]// 2nd International Conference on Robotics and Automation Engineering (ICRAE), 2017: 126-130.

    [31] LI Q, HAN G, LIU P, et al. An infrared-visible image registration method based on the constrained point feature[J]. Sensors, 2021, 21(4): 1188.

    [32] CHEN X, LIU L, ZHANG J, et al. Registration of multimodal images with edge features and scale invariant PIIFD[J]. Infrared Physics & Technology, 2020, 111: 103549.

    [33] LIU X, LI J B, PAN J S, et al. An advanced gradient texture feature descriptor based on phase information for infrared and visible image matching[J]. Multimedia Tools and Applications, 2021, 80(11): 1649116511.

    [35] CHENG T, GU J, ZHANG X, et al. Multimodal image registration for power equipment using clifford algebraic geometric invariance[J]. Energy Reports, 2022, & 1078-1086.

    [37] LIU G, LIU Z, LIU S. et al. Registration of infrared and visible light image based on visual saliency and scale invariant feature transform[J]. J. Image Video Proc., 2018: 45. https://doi.org/10.1186/s13640-018-0283-9.

    [39] MA J, ZHAO J, MA Y, et al. Non-rigid visible and infrared face registration via regularized Gaussian fields criterion [J]. Pattern Recognition, 2015, 48(3): 772-784.

    [41] MIN C, GU Y, LI Y, et al. Non-rigid infrared and visible image registration by enhanced affine transformation[J]. Pattern Recognition, 2020, 106: 107377.

    [42] MIN C, GU Y, YANG F, et al. Non-rigid registration for infrared and visible images via Gaussian weighted shape context and enhanced affine transformation[J]. IEEE Access, 2020, & 42562-42575.

    [43] ZHAO Z, ZHAO L, QI Y, et al. Infrared and visible image registration based on hypercolumns[C]//CCF Chinese Conference on Computer Vision, 2017: 529-539.

    [44] WEI Z, JUNG C, SU C. RegiNet: Gradient guided multispectral image registration using convolutional neural networks[J]. Neurocomputing, 2020, 415: 193-200.

    [45] WANG L, GAO C, ZHAO Y, et al. Infrared and visible image registration using transformer adversarial network[C]// 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018: 1248-1252.

    [46] Kumari K, Krishnamurthi G. GAN-based End-to-End Unsupervised Image Registration for RGB-Infrared Image[C]// 3rd International Conference on Intelligent Autonomous Systems QCoIAS). IEEE, 2020: 6266.

    [47] MAO Y, HE Z. Dual-Y network: infrared-visible image patches matching via semi-supervised transfer learning[J]. Applied Intelligence, 2021, 51(4): 2188-2197.

    [49] YU K, MA J, HU F, et al. A grayscale weight with window algorithm for infrared and visible image registration[J]. Infrared Physics & Technology, 2019, 99: 178-186.

    [50] LUO W, HAO X, XU C, et al. Coarse-to-fine registration for infrared and visible images of power grid[C]// 4th International Conference on Systems and Informatics (ICSAI), 2017: 1181-1185.

    LI Yunhong, LIU Yudong, SU Xueping, LUO Xuemin, YAO Lan. Review of Infrared and Visible Image Registration[J]. Infrared Technology, 2022, 44(7): 641
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