Seam-cutting methods have been proven effective in the composition step of image stitching, especially for images with parallax. However, the effectiveness of seam-cutting usually depends on that images can be roughly aligned such that there exists a local region where a plausible seam can be found. For images with large parallax, current alignment methods often fall short of expectations. In this paper, we propose a local alignment and stitching method guided by seam quality evaluation. First, we use existing image alignment and seam-cutting methods to calculate an initial seam and evaluate the quality of pixels along the seam. Then, for pixels with low qualities, we separate their enclosing patches in the aligned images and locally align them by extracting modified dense correspondences via SIFT flow. Finally, we composite the aligned patches via seam-cutting and merge them into the original aligned result to generate the final mosaic. Experiments show that compared with the state-of-the-art seam-cutting methods, our result is more plausible and with fewer artifacts. The code will be available at https://github.com/tlliao/Seam-guided-local-alignment.

翻译：接缝切割方法已被证明在图像拼接的合成步骤中有效，尤其适用于存在视差的图像。然而，接缝切割的有效性通常取决于图像能够大致对齐，使得存在一个局部区域可找到合理的接缝。对于大视差图像，现有的对齐方法往往达不到预期效果。本文提出一种由接缝质量评估引导的局部对齐与拼接方法。首先，利用现有图像对齐和接缝切割方法计算初始接缝，并评估接缝上像素的质量。然后，针对低质量像素，在已对齐图像中分离其包含的局部区域，通过SIFT流提取改进的密集对应点进行局部对齐。最后，通过接缝切割对局部对齐区域进行合成，并将其融入原始对齐结果以生成最终拼接图像。实验表明，与最先进的接缝切割方法相比，本文结果更自然且伪影更少。相关代码将开源至 https://github.com/tlliao/Seam-guided-local-alignment。