Image Redundancy Filtering for Panorama Stitching

In this paper, we designed a novel framework for massive image panorama stitching, which aims to resolve image redundancy, alignment error accumulation and perspective distortion accumulation of the stitching process. First, an iterative method is designed to filter the redundancy images by analysing the similarity relation of adjacent images. Second, to reduce alignment error accumulation, the weight topology graph of filtered images is constructed, which is employed to find the optimal reference image which closes to the central geometrically by the Floyd-Warshall algorithm. Finally, a two-step global alignment strategy is designed to initial align images and perform shape optimization, the first step is that filtered images employ the similarity model to roughly align group by group, the second step is to perform shape optimization further through refining all alignment parameters by the homography model under the anti-perspective energy term, which aims to obtain an optimal solution by balancing the alignment accuracy and the global consistency. Compared with the state-of-the-art methods, the proposed method successfully reduces image redundancy while improving the alignment and reducing perspective distortion for massive image panorama stitching.