@inproceedings{2fbc5dbdff6448e69026d427bba6407f,
title = "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.",
keywords = "Convolutional neural networks, High dynamic range video, Optical flow",
author = "Yuchen Guo and Zhifeng Xie and Wenjun Zhang and Lizhuang Ma",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71598-8\_7",
language = "英语",
isbn = "9783319715971",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "70--81",
editor = "Yao Zhao and Xiangwei Kong and David Taubman",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
address = "德国",
}