Multi-objective content preserving warping for image stitching
Image stitching has been developed to enhance users' immersive experiences by constructing new images of higher spatial resolution and broader field of view from multiple input images. Most image stitching schemes assume 2D transformation of the whole frame between images. There usually exist local misalignments of contents that do not match with the transformation and may cause artifacts such as ghosting. Several schemes including seam cutting, image blending and other image composition algorithms, have been developed to relieve such artifacts. However, these schemes are essentially post-stitching processing that do not address the alignment problem and would fail when the parallax is large. In this paper, we propose a multi-objective content preserving warping (CPW) for local adjustment of image alignment which is optimized considering feature matching, line preserving, line matching, shape smoothness and other factors to significantly reduce the visual artifacts. In particular, we adjust the image warping from their pre-warping results using the homography estimated during the image registration step. Experimental results show that the proposed CPW scheme for image stitching is able to generate much smoother stitching results comparing with the state-of-the-art methods reported in the literature.