Image stitching is to construct panoramic images with wider field of vision (FOV) from some images captured from different viewing positions. To solve the problem of fusion ghosting in the stitched image, seam-driven methods avoid the misalignment area to fuse images by predicting the best seam. Currently, as standard tools of the OpenCV library, dynamic programming (DP) and GraphCut (GC) are still the only commonly used seam prediction methods despite the fact that they were both proposed two decades ago. However, GC can get excellent seam quality but poor real-time performance while DP method has good efficiency but poor seam quality. In this paper, we propose a deep learning based seam prediction method (DSeam) for the sake of high seam quality with high efficiency. To overcome the difficulty of the seam description in network and no GroundTruth for training we design a selective consistency loss combining the seam shape constraint and seam quality constraint to supervise the network learning. By the constraint of the selection of consistency loss, we implicitly defined the mask boundaries as seams and transform seam prediction into mask prediction. To our knowledge, the proposed DSeam is the first deep learning based seam prediction method for image stitching. Extensive experimental results well demonstrate the superior performance of our proposed Dseam method which is 15 times faster than the classic GC seam prediction method in OpenCV 2.4.9 with similar seam quality.

翻译：图像拼接是指从不同拍摄位置获取的多幅图像构建具有更宽视野的全景图像。针对拼接图像中的融合鬼影问题，基于接缝驱动的方法通过预测最佳接缝避开未对准区域进行图像融合。目前，作为OpenCV库的标准工具，动态规划与图割仍是仅有的两种常用接缝预测方法，尽管它们均诞生于二十年前。然而，图割虽能获得卓越的接缝质量但实时性差，而动态规划方法效率高但接缝质量不佳。本文提出基于深度学习的接缝预测方法，旨在兼顾高接缝质量与高效率。为克服网络接缝描述困难及缺乏真实标注训练数据的难题，我们设计了一种结合接缝形状约束与接缝质量约束的选择一致性损失来监督网络学习。通过选择一致性损失的约束，我们隐式地将掩膜边界定义为接缝，并将接缝预测转化为掩膜预测。据我们所知，所提方法是首个基于深度学习的图像拼接接缝预测方法。大量实验充分证明了该方法在OpenCV 2.4.9基准测试中的优越性能——在保持与经典图割接缝预测方法相当质量的同时，速度提升15倍。