Color Event Enhanced Single-Exposure HDR Imaging
DOI:
https://doi.org/10.1609/aaai.v38i2.27904Keywords:
CV: Multi-modal Vision, CV: Computational Photography, Image & Video Synthesis, CV: Low Level & Physics-based VisionAbstract
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.Downloads
Published
2024-03-24
How to Cite
Cui, M., Wang, Z., Wang, D., Zhao, B., & Li, X. (2024). Color Event Enhanced Single-Exposure HDR Imaging. Proceedings of the AAAI Conference on Artificial Intelligence, 38(2), 1399-1407. https://doi.org/10.1609/aaai.v38i2.27904
Issue
Section
AAAI Technical Track on Computer Vision I