Image stitching method based on feature optimization and circular function weighted fusion

Abstract

To address the issues of ghosting and chromatic gaps in parallax image stitching, this paper proposes a novel method based on feature optimization and circular function weighted fusion. The method employs the Scale-Invariant Feature Transform (SIFT) algorithm with dimensionality reduction optimization to extract point features, while the Grid-based Motion Statistics (GMS) algorithm is utilized to eliminate mismatched points. To further enhance the number of features, line features are also introduced. A local warping model is employed to guide the deformation of the grid image. Finally, a nonlinear fading-in and fading-out fusion model with circular function weighting is proposed for better image transition. Experimental results demonstrate that the proposed method is effective in dealing with ghosting and chromatic gaps compared to several existing methods.