Generalized Content-Preserving Warps for Image Stitching

TL;DR

This paper introduces GCPW, a novel image stitching method that jointly estimates local colour transformations and geometric warps, improving alignment robustness amid colour variations.

Contribution

GCPW extends traditional CPW by incorporating local colour modeling, enabling effective handling of colour inconsistencies during image stitching.

Findings

Outperforms state-of-the-art CPW-based methods on synthetic and real images.

Robust to diverse colour variations in image stitching.

Joint estimation of colour and geometric transformations enhances alignment accuracy.

Abstract

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.