  Capturing videos with wrong exposure usually produces unsatisfactory visual
effects. While image exposure correction is a popular topic, the video
counterpart is less explored in the literature. Directly applying prior
image-based methods to input videos often results in temporal incoherence with
low visual quality. Existing research in this area is also limited by the lack
of high-quality benchmark datasets. To address these issues, 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. In addition, we propose a Video Exposure Correction Network
(VECNet) based on Retinex theory, which incorporates a two-stream illumination
learning mechanism to enhance the overexposure and underexposure factors,
respectively. The estimated multi-frame reflectance and dual-path illumination
components are fused at both feature and image levels, leading to visually
appealing results. Experimental results demonstrate that the proposed method
outperforms existing image exposure correction and underexposed video
enhancement methods. The code and dataset will be available soon.
