Local similarity refinement of shape-preserved warping for parallax-tolerant image stitching

This study proposes a local similarity refinement strategy to handle the parallax problem in image stitching. The proposed method is combined with deconvolution to acquire high-accuracy matching between corresponding source images. Shape-preserving half-projective warp was used to eliminate distortion across the non-overlapping region caused by the global projective transformation. The proposed refinement method further refines the warping result within the overlapping region, where it suppresses the parallax. The method was compared with various state-of-the-art methods: projective (global homography), AutoStitch, Zaragoza's method, Zhang's method, and Chang's approach. All comparisons are based on both public data sets and a proposed Inha University Computer Vision Lab (ICVL) stitching data set. The experimental results demonstrate that the proposed method is robust for handling the parallax in image stitching.