Large Parallax Image Stitching Using an Edge-Preserving Diffeomorphic Warping Process

Abstract

Image Stitching is a hard task to solve in the presence of large parallax in video frames. In many cases, video frames shot using hand-held cameras have low resolution, blur and large parallax errors. Most recent works fail to align such a sequence of images accurately. The proposed method aims to accurately align image frames, by employing a novel demon-based, edge-preserving diffeomorphic registration for image stitching, termed as “DiffeoWarps”. The first stage aligns the images globally using a mesh-based perspective (homography) transformation. At the second stage, an alternating method of minimization of correspondence energy and TV-regularization improves the alignment. The “diffeowarped” images are then blended to obtain good quality stitched results. We experimented on two standard datasets as well as on a dataset comprising of 10 sets of images/frames collected from unconstrained videos. Both qualitative and quantitative performance analysis show the superiority of our proposed method.