A Multi-dimensional Camera Image Stitching Method Under Large Parallax Conditions

Image stitching technology, as a critical link in digital image processing, can accurately combine numerous photos with overlapping sections into a panoramic image. Traditional image stitching typically demands that the input image have no parallax or very little parallax. However, in complex settings, spliced images produced by image splicing algorithms based on huge parallax are prone to issues such as ghosting and severe image distortion. This study provides an energy function optimization method for basic texture areas, as well as an enhanced seam quality evaluation algorithm to help with local seam alignment. Firstly, the entropy value based on the grey-scale covariance matrix is used to calculate the texture complexity, followed by region splitting, the penalty value of simple texture region is calculated based on the similarity of overlapping regions to obtain the energy function, secondly, the maximum flow minimum cut algorithm is used to locate the optimal seams, and finally, the simulated annealing algorithm is used to optimize the seam quality assessment algorithm for the algorithm suggested in this research performs well in picture fusion, as evidenced by experimental verification, which greatly enhances image fusion efficiency and final spliced image quality.