# Capturing Stable HDR Videos Using a Dual-Camera System

arXiv: https://arxiv.org/abs/2507.06593 · HTML: https://mlsanity.com/paper/2507.06593/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 83 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Attention-guided Network for Ghost-free High Dynamic Range Imaging](https://mlsanity.com/paper/1904.10293/) (2019)
- [HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions](https://mlsanity.com/paper/2007.01628/) (2020)
- [Ghost-free High Dynamic Range Imaging with Context-aware Transformer](https://mlsanity.com/paper/2208.05114/) (2022)
- [ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging](https://mlsanity.com/paper/2105.10697/) (2021)
- [HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset](https://mlsanity.com/paper/2103.14943/) (2021)
- [Deep Learning for HDR Imaging: State-of-the-Art and Future Trends](https://mlsanity.com/paper/2110.10394/) (2021)
- [ColorVideoVDP: A visual difference predictor for image, video and display distortions](https://mlsanity.com/paper/2401.11485/) (2024)
- [Alignment-free HDR Deghosting with Semantics Consistent Transformer](https://mlsanity.com/paper/2305.18135/) (2023)
- [HDRFlow: Real-Time HDR Video Reconstruction with Large Motions](https://mlsanity.com/paper/2403.03447/) (2024)
- [Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration](https://mlsanity.com/paper/2107.06211/) (2021)
- [SAFNet: Selective Alignment Fusion Network for Efficient HDR Imaging](https://mlsanity.com/paper/2407.16308/) (2024)
- [A Unified Framework for Multi-Sensor HDR Video Reconstruction](https://mlsanity.com/paper/1308.4908/) (2013)
- [Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction](https://mlsanity.com/paper/1810.08042/) (2018)
- [High Dynamic Range and Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2207.14671/) (2022)
- [LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2308.11116/) (2023)
- [A Unified HDR Imaging Method with Pixel and Patch Level](https://mlsanity.com/paper/2304.06943/) (2023)
- [UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data](https://mlsanity.com/paper/2102.01850/) (2021)
- [Towards Real-World HDR Video Reconstruction: A Large-Scale Benchmark Dataset and A Two-Stage Alignment Network](https://mlsanity.com/paper/2405.00244/) (2024)
- [HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors](https://mlsanity.com/paper/2103.10982/) (2021)
- [Fast High Dynamic Range Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2401.06052/) (2024)
- [AFUNet: Cross-Iterative Alignment-Fusion Synergy for HDR Reconstruction via Deep Unfolding Paradigm](https://mlsanity.com/paper/2506.23537/) (2025)
- [A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis](https://mlsanity.com/paper/1612.02761/) (2016)
- [Temporal-Mapping Photography for Event Cameras](https://mlsanity.com/paper/2403.06443/) (2024)
- [Exposure Completing for Temporally Consistent Neural High Dynamic Range Video Rendering](https://mlsanity.com/paper/2407.13309/) (2024)
- [A Dual Camera System for High Spatiotemporal Resolution Video Acquisition](https://mlsanity.com/paper/1909.13051/) (2019)
- [DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2210.04429/) (2022)

## Built on by (most cited first, 3 of 3)

- [LoCAtion: Long-time Collaborative Attention Framework for High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2603.14377/) (2026)
- [Depth-guided Multi-view Exposure Bracketing for HDR Robot Vision](https://mlsanity.com/paper/2608.16014/) (2026)
- [High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences](https://mlsanity.com/paper/2609.27274/) (2026)

## Abstract (arXiv)

High Dynamic Range (HDR) video acquisition using the alternating exposure (AE) paradigm has garnered significant attention due to its cost-effectiveness with a single consumer camera. However, despite progress driven by deep neural networks, these methods remain prone to temporal flicker in real-world applications due to inter-frame exposure inconsistencies. To address this challenge while maintaining the cost-effectiveness of the AE paradigm, we propose a novel learning-based HDR video generation solution. Specifically, we propose a dual-stream HDR video generation paradigm that decouples temporal luminance anchoring from exposure-variant detail reconstruction, overcoming the inherent limitations of the AE paradigm. To support this, we design an asynchronous dual-camera system (DCS), which enables independent exposure control across two cameras, eliminating the need for synchronization typically required in traditional multi-camera setups. Furthermore, an exposure-adaptive fusion network (EAFNet) is formulated for the DCS system. EAFNet integrates a pre-alignment subnetwork that aligns features across varying exposures, ensuring robust feature extraction for subsequent fusion, an asymmetric cross-feature fusion subnetwork that emphasizes reference-based attention to effectively merge these features across exposures, and a reconstruction subnetwork to mitigate ghosting artifacts and preserve fine details. Extensive experimental evaluations demonstrate that the proposed method achieves state-of-the-art performance across various datasets, showing the remarkable potential of our solution in HDR video reconstruction. The codes and data captured by DCS will be available at https://zqqqyu.github.io/DCS-HDR/.