Photomontage for Robust HDR Imaging with Hand-Held Cameras

Ru Li, Xiaowu He, Shuaicheng Liu, Guanghui Liu, Bing Zeng

Abstract: This paper studies the image fusion from multiple images taken by hand-held cameras with different exposures. The existing methods often generate unsatisfactory results, such as the blurring/ghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriate fusion of local regions (e.g., over or under exposed). They often require high quality image registration before fusion. However, the accurate alignment is hard to obtain in many scenarios, such as scenes with large depth variations and dynamic textures. Besides, high quality alignment is also time consuming. In this paper, we only enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, we propose to use a Markov Random Filed (MRF) function for the labelling of all pixels, which assigns different labels to different aligned input images. During the labelling, we choose well-exposured regions and skip moving objects simultaneously. Then, we combine a Laplace image according to the labels and construct the fusion result by solving the Poisson equation. We present various challenging examples to demonstrate the effectiveness and practicability of our approach.