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.

翻译：本文提出了一种新颖的手持相机连拍图像精细对齐方法。与传统的估计帧间二维变换或依赖离散对应点的技术不同，所提算法通过同时优化相机运动、每个像素的表面深度及朝向，建立了密集对应关系。该对齐方法在存在视差挑战的场景中尤其有效。基于合成连拍数据（包含微小甚至极短基线）的广泛实验表明，该方法无需任何训练即可在此类场景中超越当前最优的光流方法。除增强对齐性能外，初步结果还展现出令人期待的潜力——该方法可服务于图像修复以外的任务，如深度估计与三维重建。这使其成为各类连拍图像处理应用中的通用工具。