**4. Conclusions**

Aiming at the problem of image registration with parallax, an image registration algorithm based on deep learning and local homography transformation is proposed. A sample and label generation method suitable for local homography matrix estimation is designed by using DLT and MDLT, so as to obtain an effective image registration model through supervised learning. The proposed algorithm overcomes the defect that the existing learning-based image registration algorithm cannot be used for local homography matrix estimation and improves the weak robustness of traditional image registration algorithms. Experimental results show that the proposed algorithm achieves high image registration accuracy; low time complexity; and good robustness to illumination, color, and brightness. In particular, the combination of the proposed algorithm and a better CNN architecture can significantly improve the accuracy of image registration.

In this paper, the MDLT algorithm is adopted to generate samples with local matching points. The perturbation value cannot be set very large, otherwise it will cause unnatural deformation and dislocation of the image. Therefore, the proposed algorithm is more suitable for the sample with weak locality. In addition, compared with the traditional algorithms, the proposed algorithm has higher requirements on hardware and takes a longer time to generate samples and train neural networks; this will be improved in further work.

**Author Contributions:** Conceptualization, Y.W., M.Y. and G.J.; methodology, Y.W., M.Y. and G.J.; software, Y.W.; investigation, Z.P. and J.L.; Writing—Original draft preparation, Y.W., M.Y. and G.J.; Writing—Review and editing, M.Y. and G.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China under Grant No. 61671258, 61871247, 61931022. It was also sponsored by the K. C. Wong Magna Fund of Ningbo University.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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