doi: 10.3934/dcdss.2020198

Night panoramic image stitching algorithm based on cyclic symmetric structure

1. 

Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul 06974, Korea

2. 

Department of Environmental Art Design, Arts Academy, Qingdao University of Science and Technology, Qingdao 266061, China

3. 

Medical Beauty, The Affiliated Hospital of Qingdao University, Qingdao 266000, China

* Corresponding author: Joonki Paik

Received  March 2019 Revised  April 2019 Published  December 2019

Aiming at the problem that the current stitching image has poor visual effect and low stitching efficiency when performing the night panoramic image stitching task, a night panoramic image stitching algorithm based on cyclic symmetric structure is proposed. In this algorithm, appropriate image acquisition method is used and the image obtained by the acquisition is smoothed, edge sharpened, geometrically corrected, etc. The position of the key point of the preprocessed image and the corresponding scale are determined by Gaussian function, and the direction value is assigned to the feature point to generate the feature descriptor, so as to realize the image edge feature extraction; on this basis, the edge feature points are hierarchically segmented according to the topological structure of the edge feature point set of target image, and the edge feature point registration of night panoramic image is completed; the cyclic symmetric structure is used to make the discrete Fourier transform of the target image, in order to reduce the amount of calculation; according to the gradient value and correlation coefficient of each pixel neighborhood in the overlapping region, the stitching line is searched to overcome the ghost image of the image after registration and realize seamless stitching of night panoramic image. The experimental results show that the proposed algorithm can effectively preserve the texture information of the original image while eliminating the smooth transition of the stitching seam, and at the same time, it has better stitching effect and stitching efficiency for the images under different conditions.

Citation: Sun Hao, Yuanxin Miao, Joonki Paik. Night panoramic image stitching algorithm based on cyclic symmetric structure. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020198
References:
[1]

F. Bellavia and C. Colombo, Dissecting and reassembling color correction algorithms for image stitching, IEEE Trans. Image Process., 27 (2018), 735-748.  doi: 10.1109/TIP.2017.2757262.  Google Scholar

[2]

J. ChenK. Wang and W. Yang, Accurate estimation of stitching error for high-resolution multi-sub-bands SAR system with image sharpness analysis, Electronics Lett., 23 (2016), 1948-1950.  doi: 10.1049/el.2016.3087.  Google Scholar

[3]

J. H. CuiJ. Y. YangW. W. Zhang and et al., Simulation of zinc oxide battery cathode material distribution based on digital image technology, Chinese J. Power Sources, 10 (2017), 1424-1426.   Google Scholar

[4]

R. Desai and B. P. Patil, Adaptive routing based on predictive reinforcement learning, Internat. J. Comput. Appl., 40 (2018), 73-81.  doi: 10.1080/1206212X.2017.1395106.  Google Scholar

[5]

W. Gao and W. A. Wang, A tight neighborhood union condition on fractional (g, f, n', m)-critical deleted graphs, Colloq. Math., 149 (2017), 291-298.  doi: 10.4064/cm6959-8-2016.  Google Scholar

[6]

W. GaoL. ZhuY. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, J. Intelligent Fuzzy Syst., 33 (2017), 3153-3163.  doi: 10.3233/JIFS-169367.  Google Scholar

[7]

N. M. Gonzalez, T. C. M. D. Carvalho and C. C. Miers, Cloud resource management: Towards efficient execution of large-scale scientific applications and workflows on complex infrastructures, J. Cloud Comput., 6 (2017). doi: 10.1186/s13677-017-0081-4.  Google Scholar

[8]

S. M. Hosamani, Correlation of domination parameters with physicochemical properties of octane isomers, Appl. Math. Nonlinear Sci., 1 (2016), 345-351.  doi: 10.21042/AMNS.2016.2.00029.  Google Scholar

[9]

M. T. IbrahimM. M. KhanM. M. Khan and Y. J. Cho, Automatic selection of color reference image for panoramic stitching, Multimedia Syst., 3 (2016), 379-392.  doi: 10.1007/s00530-015-0467-4.  Google Scholar

[10]

K. Kalirajan and M. Sudha, Moving object detection using median-based scale invariant local ternary pattern for video surveillance system, Journal of Intelligent and Fuzzy Systems, 33 (2017), 1933-1943.  doi: 10.3233/JIFS-162231.  Google Scholar

[11]

B. W. Lei and Y. Shi, Matching method for image stitching of inner wall of thread barrel, J. Henan University Sci. Tech. (Natural Sci.), 5 (2017), 37-42.   Google Scholar

[12]

W. LiC. B. JinM. Liu and et al., Local similarity refinement of shape-preserved warping for parallax-tolerant image stitching., IET Image Process., 12 (2015), 661-668.  doi: 10.1049/iet-ipr.2017.0037.  Google Scholar

[13]

Y. LiL. LingT. Yang and et al., Analysis and research on panoramic parking assistance technology and image seamless stitching technology, Automat. Instrumentation, 9 (2017), 47-49.   Google Scholar

[14]

J. LiZ. WangS. Lai and et al., Parallax-tolerant image stitching based on robust elastic warping, IEEE Trans. Multimedia, 20 (2018), 1672-1687.  doi: 10.1109/TMM.2017.2777461.  Google Scholar

[15]

Y. X. ShenJ. Wu and D. H. Wu, Diagnosis technology for three-level inverter based on reconstructive phase space and SVM, J. Power Supply, 6 (2017), 108-115.   Google Scholar

[16]

Z. J. Wang, Characteristics of pixel matching research on image patch merging simulation, Comput. Simulation, 6 (2016), 201-204.   Google Scholar

[17]

X. Y. Wang, Multi-gray distortion image stitching based on nonlinear equation, J. China Academy Electronics Information Tech., 6 (2017), 662-667.   Google Scholar

[18]

Y. YangM. ZhongH. Yao and et al., Internet of things for smart ports: Technologies and challenges, IEEE Instrumentation and Measurement Magazine, 21 (2018), 34-43.  doi: 10.1109/MIM.2018.8278808.  Google Scholar

[19]

D. YuH. ZhuW. Han and D. Holburn, Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system, Energy, 173 (2019), 554-568.  doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

N. N. Zhu, Simulation analysis of cylindrical panoramic image stitching, Acta Geodaetica et Cartographica Sinica, 4 (2017), 487-497.   Google Scholar

[21]

Q. H. ZhuY. Y. ShangZ. H. Shao and Y. Ye, Cylindrical panorama stitching algorithm based on local features and vision consistence, J. Image Graphics, 11 (2018), 1523-1529.   Google Scholar

[22]

Z. H. ZhuJ. Y. FuJ. S. Yang and et al., Panoramic image stitching for arbitrarily shaped tunnel lining inspection, Comput.-Aided Civil Infrastructure Eng., 31 (2016), 936-953.  doi: 10.1111/mice.12230.  Google Scholar

[23]

S. ZhuL. Liu and S. Chen, Image feature detection algorithm based on the spread of Hessian source, Multimedia Syst., 23 (2017), 105-117.  doi: 10.1007/s00530-015-0453-x.  Google Scholar

show all references

References:
[1]

F. Bellavia and C. Colombo, Dissecting and reassembling color correction algorithms for image stitching, IEEE Trans. Image Process., 27 (2018), 735-748.  doi: 10.1109/TIP.2017.2757262.  Google Scholar

[2]

J. ChenK. Wang and W. Yang, Accurate estimation of stitching error for high-resolution multi-sub-bands SAR system with image sharpness analysis, Electronics Lett., 23 (2016), 1948-1950.  doi: 10.1049/el.2016.3087.  Google Scholar

[3]

J. H. CuiJ. Y. YangW. W. Zhang and et al., Simulation of zinc oxide battery cathode material distribution based on digital image technology, Chinese J. Power Sources, 10 (2017), 1424-1426.   Google Scholar

[4]

R. Desai and B. P. Patil, Adaptive routing based on predictive reinforcement learning, Internat. J. Comput. Appl., 40 (2018), 73-81.  doi: 10.1080/1206212X.2017.1395106.  Google Scholar

[5]

W. Gao and W. A. Wang, A tight neighborhood union condition on fractional (g, f, n', m)-critical deleted graphs, Colloq. Math., 149 (2017), 291-298.  doi: 10.4064/cm6959-8-2016.  Google Scholar

[6]

W. GaoL. ZhuY. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, J. Intelligent Fuzzy Syst., 33 (2017), 3153-3163.  doi: 10.3233/JIFS-169367.  Google Scholar

[7]

N. M. Gonzalez, T. C. M. D. Carvalho and C. C. Miers, Cloud resource management: Towards efficient execution of large-scale scientific applications and workflows on complex infrastructures, J. Cloud Comput., 6 (2017). doi: 10.1186/s13677-017-0081-4.  Google Scholar

[8]

S. M. Hosamani, Correlation of domination parameters with physicochemical properties of octane isomers, Appl. Math. Nonlinear Sci., 1 (2016), 345-351.  doi: 10.21042/AMNS.2016.2.00029.  Google Scholar

[9]

M. T. IbrahimM. M. KhanM. M. Khan and Y. J. Cho, Automatic selection of color reference image for panoramic stitching, Multimedia Syst., 3 (2016), 379-392.  doi: 10.1007/s00530-015-0467-4.  Google Scholar

[10]

K. Kalirajan and M. Sudha, Moving object detection using median-based scale invariant local ternary pattern for video surveillance system, Journal of Intelligent and Fuzzy Systems, 33 (2017), 1933-1943.  doi: 10.3233/JIFS-162231.  Google Scholar

[11]

B. W. Lei and Y. Shi, Matching method for image stitching of inner wall of thread barrel, J. Henan University Sci. Tech. (Natural Sci.), 5 (2017), 37-42.   Google Scholar

[12]

W. LiC. B. JinM. Liu and et al., Local similarity refinement of shape-preserved warping for parallax-tolerant image stitching., IET Image Process., 12 (2015), 661-668.  doi: 10.1049/iet-ipr.2017.0037.  Google Scholar

[13]

Y. LiL. LingT. Yang and et al., Analysis and research on panoramic parking assistance technology and image seamless stitching technology, Automat. Instrumentation, 9 (2017), 47-49.   Google Scholar

[14]

J. LiZ. WangS. Lai and et al., Parallax-tolerant image stitching based on robust elastic warping, IEEE Trans. Multimedia, 20 (2018), 1672-1687.  doi: 10.1109/TMM.2017.2777461.  Google Scholar

[15]

Y. X. ShenJ. Wu and D. H. Wu, Diagnosis technology for three-level inverter based on reconstructive phase space and SVM, J. Power Supply, 6 (2017), 108-115.   Google Scholar

[16]

Z. J. Wang, Characteristics of pixel matching research on image patch merging simulation, Comput. Simulation, 6 (2016), 201-204.   Google Scholar

[17]

X. Y. Wang, Multi-gray distortion image stitching based on nonlinear equation, J. China Academy Electronics Information Tech., 6 (2017), 662-667.   Google Scholar

[18]

Y. YangM. ZhongH. Yao and et al., Internet of things for smart ports: Technologies and challenges, IEEE Instrumentation and Measurement Magazine, 21 (2018), 34-43.  doi: 10.1109/MIM.2018.8278808.  Google Scholar

[19]

D. YuH. ZhuW. Han and D. Holburn, Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system, Energy, 173 (2019), 554-568.  doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

N. N. Zhu, Simulation analysis of cylindrical panoramic image stitching, Acta Geodaetica et Cartographica Sinica, 4 (2017), 487-497.   Google Scholar

[21]

Q. H. ZhuY. Y. ShangZ. H. Shao and Y. Ye, Cylindrical panorama stitching algorithm based on local features and vision consistence, J. Image Graphics, 11 (2018), 1523-1529.   Google Scholar

[22]

Z. H. ZhuJ. Y. FuJ. S. Yang and et al., Panoramic image stitching for arbitrarily shaped tunnel lining inspection, Comput.-Aided Civil Infrastructure Eng., 31 (2016), 936-953.  doi: 10.1111/mice.12230.  Google Scholar

[23]

S. ZhuL. Liu and S. Chen, Image feature detection algorithm based on the spread of Hessian source, Multimedia Syst., 23 (2017), 105-117.  doi: 10.1007/s00530-015-0453-x.  Google Scholar

Figure 1.  Reference image
Figure 2.  Image to be registered
Figure 3.  Stitching effect of the algorithm in reference [6]
Figure 4.  Stitching effect of the algorithm in reference [7]
Figure 5.  Stitching effect of the proposed algorithm
Table 1.  Comparison of the evaluation indexes of image stitching effect
Evaluation index Proposed algorithm Algorithm in the reference [6] Algorithm in the reference [7]
Information entropy 7.5342 7.3185 7.1799
Standard deviation 53.8867 52.9017 48.3676
Mutual information 8.5249 8.5407 8.5574
Average gradient 0.9982 0.9924 0.9779
Evaluation index Proposed algorithm Algorithm in the reference [6] Algorithm in the reference [7]
Information entropy 7.5342 7.3185 7.1799
Standard deviation 53.8867 52.9017 48.3676
Mutual information 8.5249 8.5407 8.5574
Average gradient 0.9982 0.9924 0.9779
Table 2.  Comparison of parameters of ghost removal effect
Experimental team Evaluation index Algorithm in the reference [6] Algorithm in the reference [7] Proposed algorithm
Group with small gray difference Spatial frequency activity 0.0265 0.0366 0.0264
Structural contrast 1.2292 1.2002 1.1602
Comprehensive evaluation index constructed by the two 1.2353 1.3974 1.1763
Group with large gray difference Spatial frequency activity 0.1047 0.1054 0.1044
Structural contrast 1.3795 1.2764 1.1345
Comprehensive evaluation index constructed by the two 1.4094 1.3622 1.2847
Regular texture group Spatial frequency activity 0.0342 0.0342 0.0342
Structural contrast 1.2280 1.3244 1.2343
Comprehensive evaluation index constructed by the two 1.3667 1.4122 1.3635
Irregular texture group Spatial frequency activity 0.1652 0.0602 0.0545
Structural contrast 1.2215 1.0002 0.9675
Comprehensive evaluation index constructed by the two 1.4122 0.8829 0.8848
Experimental team Evaluation index Algorithm in the reference [6] Algorithm in the reference [7] Proposed algorithm
Group with small gray difference Spatial frequency activity 0.0265 0.0366 0.0264
Structural contrast 1.2292 1.2002 1.1602
Comprehensive evaluation index constructed by the two 1.2353 1.3974 1.1763
Group with large gray difference Spatial frequency activity 0.1047 0.1054 0.1044
Structural contrast 1.3795 1.2764 1.1345
Comprehensive evaluation index constructed by the two 1.4094 1.3622 1.2847
Regular texture group Spatial frequency activity 0.0342 0.0342 0.0342
Structural contrast 1.2280 1.3244 1.2343
Comprehensive evaluation index constructed by the two 1.3667 1.4122 1.3635
Irregular texture group Spatial frequency activity 0.1652 0.0602 0.0545
Structural contrast 1.2215 1.0002 0.9675
Comprehensive evaluation index constructed by the two 1.4122 0.8829 0.8848
Table 3.  Time-consuming of general image stitching
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.450 1.555 0.136
Descriptor generation time/s 17.056 7.437 8.123
Stitching time/s 10.540 2.445 1.286
Total time 30.046 11.437 9.545
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.450 1.555 0.136
Descriptor generation time/s 17.056 7.437 8.123
Stitching time/s 10.540 2.445 1.286
Total time 30.046 11.437 9.545
Table 4.  Time-consuming of image stitching wit uneven illumination
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.274 1.453 0.115
Descriptor generation time/s 12.502 3.274 5.126
Stitching time/s 8.869 2.158 1.130
Total time 23.654 6.885 6.371
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.274 1.453 0.115
Descriptor generation time/s 12.502 3.274 5.126
Stitching time/s 8.869 2.158 1.130
Total time 23.654 6.885 6.371
Table 5.  Time-consuming of rotation image stitching
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.161 1.439 0.117
Descriptor generation time/s 10.359 3.322 5.480
Stitching time/s 7.794 2.979 1.198
Total time 20.314 7.740 6.795
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.161 1.439 0.117
Descriptor generation time/s 10.359 3.322 5.480
Stitching time/s 7.794 2.979 1.198
Total time 20.314 7.740 6.795
Table 6.  Time-consuming of scaling image stitching
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.472 1.548 0.130
Descriptor generation time/s 17.131 3.484 5.509
Stitching time/s 8.612 2.845 1.224
Total time 28.215 7.877 6.683
Time consuming/s Algorithm Algorithm in reference [6] Algorithm in reference [7] Proposed algorithm
Time-consuming for feature extraction/s 2.472 1.548 0.130
Descriptor generation time/s 17.131 3.484 5.509
Stitching time/s 8.612 2.845 1.224
Total time 28.215 7.877 6.683
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