Super High Dynamic Range Video

High dynamic range (HDR) imaging is highly demanded in computer vision algorithms. An HDR image is composed with several low dynamic range (LDR) images, which usually have some disparities. In many HDR imaging algorithms, the disparities are estimated based on the texture information of the LDR images. However, the texture information is often lost completely if scenes include extremely bright and dark regions simultaneously. Recently, super high dynamic range (SHDR) imaging algorithm has been proposed where the disparities are estimated based on the segment shapes instead of the textures for handling such extreme scenes. In this paper, we extend the SHDR imaging algorithm to SHDR video generation introducing temporal smoothness terms. The temporal smoothness terms improve the temporal stability and the precision of the disparity estimation. Quantitative and qualitative evaluations demonstrate that the proposed algorithm outperforms existing algorithms.