Efficient high dynamic range video using multi-exposure CNN flow

Abstract

High dynamic range (HDR) imaging has seen a lot of progress in recent years, while an efficient way to capture and generate HDR video is still in need. In this paper, we present a method to generate HDR video from frame sequence of alternating exposures in a fast and concise fashion. It takes advantage of the recent advancement in deep learning to achieve superior efficiency compared to other state-of-art method. By training an end-to-end CNN model to estimate optical flow between frames of different exposures, we are able to achieve dense image registration of them. Using this as a base, we develop an efficient method to reconstruct the aligned LDR frames with different exposure and then merge them to produce the corresponding HDR frame. Our approach shows good performance and time efficiency while still maintain a relatively concise framework.