Multi-exposure photomontage with hand-held cameras

The paper studies the image fusion from multiple images taken by hand-held cameras with different exposures. Existing methods often generate unsatisfactory results, such as blurring/ghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriately fusion of local regions (e.g., over or under exposed). In addition, they often require a high-quality image registration, which is hard to achieve in scenarios with large depth variations and dynamic textures, and is also time-consuming. In this paper, we propose to enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, the method first uses a Markov Random Field (MRF) energy for the labeling of all pixels, which assigns different labels to different aligned input images. During the labeling, it chooses well-exposed regions and skips moving objects at the same time. Then, the proposed method combines a Laplacian image according to the labels and constructs the fusion result by solving the Poisson equation. Furthermore, it adds some internal constraints when solving the Poisson equation for balancing and improving fusion results. We present various challenging examples, including static/dynamic, indoor/outdoor and daytime/nighttime scenes, to demonstrate the effectiveness and practicability of the proposed method.