Fine Dense Alignment of Image Bursts through Camera Pose and Depth
  Estimation
Bruno Lecouat Yann Dubois de Mont-Marin Théo Bodrito Julien Mairal Jean Ponce
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.