Color Event Enhanced Single-Exposure HDR Imaging
Mengyao Cui, Zhigang Wang, Dong Wang, Bin Zhao, Xuelong Li
Abstract
Single-exposure high dynamic range (HDR) imaging aims to reconstruct the wide-range intensities of a scene by using its single low dynamic range (LDR) image, thus providing significant efficiency. Existing methods pay high attention to restoring the luminance by inversing the tone-mapping process, while the color in the over-/under-exposed area cannot be well restored due to the information loss of the single LDR image. To address this issue, we introduce color events into the imaging pipeline, which record asynchronous pixel-wise color changes in a high dynamic range, enabling edge-like scene perception under challenging lighting conditions. Specifically, we propose a joint framework that incorporates color events and a single LDR image to restore both content and color of an HDR image, where an exposureaware transformer (EaT) module is designed to propagate the informative hints, provided by the normal-exposed LDR regions and the event streams, to the missing areas. In this module, an exposure-aware mask is estimated to suppress distractive information and strengthen the restoration of the over-/under-exposed regions. To our knowledge, we are the first to use color events to enhance single-exposure HDR imaging. We also contribute corresponding datasets, consisting of synthesized datasets and a real-world dataset collected by a DAVIS346-color camera. The datasets can be found at https://www.kaggle.com/datasets/mengyaocui/ce-hdr. Extensive experiments demonstrate the effectiveness of the proposed method.
BibTeX
@article{Cui_Wang_Wang_Zhao_Li_2024, title={Color Event Enhanced Single-Exposure HDR Imaging}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27904}, DOI={10.1609/aaai.v38i2.27904}, abstractNote={Single-exposure high dynamic range (HDR) imaging aims
to reconstruct the wide-range intensities of a scene by using
its single low dynamic range (LDR) image, thus providing
significant efficiency. Existing methods pay high attention to
restoring the luminance by inversing the tone-mapping process,
while the color in the over-/under-exposed area cannot
be well restored due to the information loss of the single
LDR image. To address this issue, we introduce color
events into the imaging pipeline, which record asynchronous
pixel-wise color changes in a high dynamic range, enabling
edge-like scene perception under challenging lighting conditions.
Specifically, we propose a joint framework that incorporates
color events and a single LDR image to restore
both content and color of an HDR image, where an exposureaware
transformer (EaT) module is designed to propagate the
informative hints, provided by the normal-exposed LDR regions
and the event streams, to the missing areas. In this
module, an exposure-aware mask is estimated to suppress
distractive information and strengthen the restoration of the
over-/under-exposed regions. To our knowledge, we are the
first to use color events to enhance single-exposure HDR
imaging. We also contribute corresponding datasets, consisting
of synthesized datasets and a real-world dataset collected
by a DAVIS346-color camera. The datasets can be found at
https://www.kaggle.com/datasets/mengyaocui/ce-hdr. Extensive
experiments demonstrate the effectiveness of the proposed
method.}, number={2}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cui, Mengyao and Wang, Zhigang and Wang, Dong and Zhao, Bin and Li, Xuelong}, year={2024}, month={Mar.}, pages={1399-1407} }