This paper introduces a novel approach to the fine alignment of images in a
burst captured by a handheld camera. In contrast to traditional techniques that
estimate two-dimensional transformations between frame pairs or rely on
discrete correspondences, the proposed algorithm establishes dense
correspondences by optimizing both the camera motion and surface depth and
orientation at every pixel. This approach improves alignment, particularly in
scenarios with parallax challenges. Extensive experiments with synthetic bursts
featuring small and even tiny baselines demonstrate that it outperforms the
best optical flow methods available today in this setting, without requiring
any training. Beyond enhanced alignment, our method opens avenues for tasks
beyond simple image restoration, such as depth estimation and 3D
reconstruction, as supported by promising preliminary results. This positions
our approach as a versatile tool for various burst image processing
applications.

本文介绍了一种新颖的方法来对手持相机拍摄的图像进行细微对齐。与传统的技术不同，该算法通过优化每个像素的摄像机运动、表面深度和方向来建立密集对应关系。这种方法改善了在具有视差挑战的情况下的对齐效果。通过对具有小型或短小基线的合成连拍进行广泛实验证明，在该环境中，该方法优于当今最佳的光流方法，而且无需任何训练。除了改进对齐效果，我们的方法还为深度估计和 3D 重建等其他图像处理任务打开了新的可能性，并取得了有希望的初步结果，使我们的方法成为各种连拍图像处理应用的多功能工具。