Wuxia Yan1,*, Rongxin Zhu1,2, Yan Cui1
1Nanjing Normal University of Special Education, Nanjing 210038, Jiangsu, China.
2Hainan University, Haikou 570228, Hainan, China.
*Corresponding author: Wuxia Yan
Abstract
Image stitching is one of the core technologies for virtual scene construction in virtual reality technology, and there are always artifacts, ghosting, distortion and other problems in the panoramas obtained by image stitching. In order to quickly and robustly stitch images and obtain high-quality panoramas, an image stitching method based on content optimisation is proposed. The optimisation rules of local content similarity preservation and linear similarity preservation can ensure the invariance of the basic content as well as the line segments in the image before and after stitching, so as to minimise the generation of artifacts, ghosting, and distortion in the panoramia. The method is based on the framework of coarse matching + fine matching, which firstly obtains the image homography transform model according to the matching feature points to achieve the initial alignment of the image, and then performs the content optimisation according to the optimisation rules to reduce the generation of artifacts and distortion to obtain a high-quality panorama. The experimental comparison results with similar methods show that the proposed method can quickly obtain high-quality spliced images with less artefacts and distortions.
References
Chen, Y., Zheng, H., Yan, Z., et al. (2022). Parallax image alignment based on two-step mesh optimization with homography diffusion constraints. Acta Automatica Sinica, 48(x), 1-14.
Lin, K., Jiang, N., & Liu, S. (2017). Direct photometric alignment by mesh deformation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2701-2709).
Lin, K., Jiang, N., Cheong, L. F., et al. (2016). SEAGULL: Seam-guided local alignment for parallax-tolerant image stitching. In European Conference on Computer Vision (pp. 370-385).
Liu, F., Gleicher, M., Jin, H., et al. (2009). Content-preserving warps for 3D video stabilization. ACM Transactions on Graphics, 44, 1-9.
Ministry of Industry and Information Technology, Ministry of Education, Ministry of Culture and Tourism, National Radio and Television Administration, & General Administration of Sport. (2022). Virtual reality and industry application integration development action plan (2022-2026) [Notice]. Retrieved from
https://www.gov.cn/zhengce/zhengceku/2022-11/01/content_5723273.htm
Shen, X., Darmon, F., Efros, A. A., et al. (2020). RANSAC-Flow: Generic two-stage image alignment. In European Conference on Computer Vision (pp. 618-637).
Zaragoza, J., Chin, T. J., Brown, M. S., et al. (2013). As-projective-as-possible image stitching with moving DLT. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2339-2346).
Zhang, F., & Liu, F. (2014). Parallax-tolerant image stitching. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3262-3269).
How to cite this paper
Image Stitching Method Based on Content Optimization in Virtual Reality
How to cite this paper: Wuxia Yan, Rongxin Zhu, Yan Cui. (2026). Image Stitching Method Based on Content Optimization in Virtual Reality. Open Journal of Image Processing and Computer Vision, 1(1), 13-19.
DOI: http://dx.doi.org/10.26855/ojipcv.2026.12.003