@inproceedings{
liu2024learning,
title={Learning Exposure Correction in Dynamic Scenes},
author={Jin Liu and Bo Wang and Chuanming Wang and Huiyuan Fu and Huadong Ma},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=fylmVagDag}
}
Exposure correction aims to enhance visual data suffering from improper exposures, which can greatly improve satisfactory visual effects. However, previous methods mainly focus on the image modality, and the video counterpart is less explored in the literature. Directly applying prior image-based methods to videos results in temporal incoherence with low visual quality. Through thorough investigation, we find that the development of relevant communities is limited by the absence of a benchmark dataset. Therefore, in this paper, we construct the first real-world paired video dataset, including both underexposure and overexposure dynamic scenes. To achieve spatial alignment, we utilize two DSLR cameras and a beam splitter to simultaneously capture improper and normal exposure videos. Additionally, we propose an end-to-end Video Exposure Correction Network (VECNet), in which a dual-stream module is designed to deal with both underexposure and overexposure factors, enhancing the illumination based on Retinex theory. Experimental results based on various metrics and user studies demonstrate the significance of our dataset and the effectiveness of our method. The code and dataset will be available soon.
Learning Exposure Correction in Dynamic Scenes
Jin Liu Bo Wang Chuanming Wang Huiyuan Fu Huadong Ma