Abstract: To address the inefficiency and dynamic object distortion issues in asynchronous image stitching under dynamic scenes,this paper proposes a parameter-adaptive image stitching method based on the Grey Wolf Optimizer (GWO) algorithm.This method integrates the swarm intelligence search mechanism of GWO into the RANSAC framework,mapping keypoint subsets as “wolf pack individuals” and utilizing the guided search mechanism of α,β,δ wolves to make keypoint selection “optimization-oriented”,ensuring the integrity and continuity of dynamic objects in asynchronous states.Additionally,to improve the robustness and processing efficiency of the algorithm in different scenarios,a two-stage parameter adaptation mechanism is introduced,including low-resolution pre-computation and dynamic termination conditions,achieving automated adjustment of core parameters such as error tolerance and iteration count.Experiments on the StabStitch-D dataset show that under the same iterative conditions,GWO-RANSAC improves the inlier matching rate by 4.91% compared to traditional RANSAC,PSNR value increases by 11.4% (from 32.5dB to 36.2dB) and SSIM value increases by 5.1% (from 0.881 to 0.926),while effectively reducing black borders and misalignment phenomena in stitched images,and ensuring the integrity and continuity of dynamic objects even in complex scenarios.Theoretical analysis shows that this method has significant advantages in resource-constrained environments and dynamic asynchronous scenarios,forming effective complementarity with deep learning methods.

Key words: Dynamic image stitching, Heuristic methods, RANSAC, Grey wolf optimizer, Parameter-adaptive