High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences

2026-09-22

AI Summary

本文提出了一种端到端框架RawHDRV，通过利用Bayer数据的线性响应和通道特性，解决了从单曝光原始视频序列中高质量重建高动态范围（HDR）视频的问题。

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.

Problem

• High Dynamic Range
• Single-Exposure
• Bayer Data
• Highlight Clipping
• Shadow Detail Loss

Innovation

• single-exposure
• channel-decomposition
• exposure complementarity mask
• mask-guided color loss
• temporal alignment