Single-exposure raw video improves highlight and shadow details
High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences
Computer Vision and Pattern Recognition
Summary
Cameras usually capture videos with limited brightness range, causing bright spots to be too bright and dark areas to lose detail. The authors introduce RawHDRV, a method that uses the raw sensor data from a single exposure to recover these details better without needing multiple exposures or special hardware. Their approach processes different color channels separately to handle brightness better and uses smart ways to combine multiple frames to improve the image quality. They also created a large dataset to test their method, proving it works well even in challenging bright or dark scenes.
What this means in practice
- •For mobile camera developers: Improve HDR video quality from single-shot raw camera data without requiring multiple exposures or extra hardware.$Commercial implications: Enables camera manufacturers to build better HDR video processing into smartphones, improving video quality under difficult lighting.
- •For video post-production teams: Restore highlight and shadow details in raw video footage when enhancing videos captured under a single exposure.
Authors
Tao Zhang, Peixian Su, Xingyu Gao, Yunhao Zou, Yu Lu, Zunjie Zhu, Bolun Zheng, Ying Fu, Chenggang Yan
Abstract
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic range (HDR) reconstruction from single-exposure sequences highly challenging without alternating exposures or extra hardware. Alternating-exposure HDR methods sacrifice frame rate and struggle with motion alignment, making them impractical for real-world capture. To address this, we propose RawHDRV, an end-to-end framework for single-exposure Raw video HDR reconstruction, that fundamentally exploits the linear response and channel-specific characteristics of Bayer data. Specifically, it features a channel-decomposition temporal alignment and fusion strategy that processes Bayer channels separately to exploit their distinct exposure characteristics, together with exposure-aware weighted fusion. It further incorporates an exposure complementarity mask-guided restoration module that leverages inter-frame exposure redundancy to adaptively fuse reliable information and suppress saturation artifacts, and introduces a mask-guided color loss that combines normalized error constraints with gradient smoothing to enhance highlight recovery. Furthermore, we construct a large-scale mobile Raw-HDR video dataset with per-frame HDR annotations. Experiments show that our method achieves the state-of-the-art results in all metrics, demonstrating superior spatial quality and temporal stability under extreme exposure conditions. The code is available at https://github.com/supeixian/RawHDRV.