Parallax image stitching based on subdivision mesh and deviation correction

In order to solve the problem of feature point registration and local alignment in parallax image stitching, we proposed an image stitching algorithm based on grid refinement and deviation correction, and optimizes feature points through random sample consensus (RANSAC) and normal distribution theory. Refine the grid according to the distribution of matching points, use the moving direct linear transformation (MDLT) to calculate the local homography matrix and combine with the global optimal similarity matrix to complete the grid distortion, and apply the thin plate spline (TPS) theory to correct the local projection deviation. To achieve better alignment of images in local overlapping areas. The experimental results show that the method in this paper has obvious advantages compared with other advanced algorithms in stitching quality and stitching speed, which proves that the algorithm in this paper has the feasibility of practical application in image mosaic work.