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Low-textured image stitching remains a challenging problem. It is difficultto achieve good alignment and it is easy to break image structures due toinsufficient and unreliable point correspondences. Moreover, because of theviewpoint variations between multiple images, the stitched images suffer fromprojective distortions. To solve these problems, this paper presents aline-guided local warping method with a global similarity constraint for imagestitching. Line features which serve well for geometric descriptions and sceneconstraints, are employed to guide image stitching accurately. On one hand, theline features are integrated into a local warping model through a designedweight function. On the other hand, line features are adopted to impose stronggeometric constraints, including line correspondence and line colinearity, toimprove the stitching performance through mesh optimization. To mitigateprojective distortions, we adopt a global similarity constraint, which isintegrated with the projective warps via a designed weight strategy. Thisconstraint causes the final warp to slowly change from a projective to asimilarity transformation across the image. Finally, the images undergo atwo-stage alignment scheme that provides accurate alignment and reducesprojective distortion. We evaluate our method on a series of images and compareit with several other methods. The experimental results demonstrate that theproposed method provides a convincing stitching performance and that itoutperforms other state-of-the-art methods.

Image Stitching by Line-guided Local Warping with Global Similarity Constraint