Depth maps and high-dynamic range image generation from alternating exposure multiview images

Abstract

For stereo matching, it is hard to find accurate correspondence for saturated regions, such as too dark or too bright regions, because there is rarely reliable information to match. In this situation, conventional high-dynamic range (HDR) imaging techniques combining multiple exposures for each viewpoint can be adopted to generate well-exposed stereo images. This approach is, however, time-consuming and needs much memory to store multiple exposures for each viewpoint. We propose an efficient method to generate HDR multiview images as well as corresponding accurate depth maps. First, we take a single exposure for each viewpoint with alternating exposure setting, such as short and long exposure, as functions of viewpoint changes. Then, we compute an initial depth map for each view only using neighboring images that have the same exposure. To reduce the error of the initial depth maps for the saturated regions, we adopt the fusion move algorithm fusing neighboring depth maps that have different error regions. Finally, using the enhanced depth maps, we generate artifact-free and sharp HDR images using the joint bilateral filtering and a detail-transfer technique. Experimental results show that our method produces both consistent HDR images and accurate depth maps for various indoor and outdoor multiview images.