Generalized Content-Preserving Warps for Image Stitching
This work addresses color inconsistency issues in image stitching for applications like photography and computer vision, representing an incremental improvement over existing CPW methods.
The paper tackles local misalignment in image stitching by proposing Generalized Content-Preserving Warping (GCPW), which extends CPW with a local color model to handle color variations, resulting in robust performance that outperforms state-of-the-art CPW-based methods on synthetic and real images.
Local misalignment caused by global homography is a common issue in image stitching task. Content-Preserving Warping (CPW) is a typical method to deal with this issue, in which geometric and photometric constraints are imposed to guide the warping process. One of its essential condition however, is colour consistency, and an elusive goal in real world applications. In this paper, we propose a Generalized Content-Preserving Warping (GCPW) method to alleviate this problem. GCPW extends the original CPW by applying a colour model that expresses the colour transformation between images locally, thus meeting the photometric constraint requirements for effective image stitching. We combine the photometric and geometric constraints and jointly estimate the colour transformation and the warped mesh vertexes, simultaneously. We align images locally with an optimal grid mesh generated by our GCPW method. Experiments on both synthetic and real images demonstrate that our new method is robust to colour variations, outperforming other state-of-the-art CPW-based image stitching methods.