# A Maximum A Posteriori Estimation Framework for Robust High Dynamic Range Video Synthesis

arXiv: https://arxiv.org/abs/1612.02761 · HTML: https://mlsanity.com/paper/1612.02761/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2016.

## Built on by (most cited first, 9 of 9)

- [HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset](https://mlsanity.com/paper/2103.14943/) (2021)
- [LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2308.11116/) (2023)
- [HDR Video Reconstruction with a Large Dynamic Dataset in Raw and sRGB Domains](https://mlsanity.com/paper/2304.04773/) (2023)
- [Diffusion-Promoted HDR Video Reconstruction](https://mlsanity.com/paper/2406.08204/) (2024)
- [HDRVideo-GAN: Deep Generative HDR Video Reconstruction](https://mlsanity.com/paper/2110.11795/) (2021)
- [DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2210.04429/) (2022)
- [Capturing Stable HDR Videos Using a Dual-Camera System](https://mlsanity.com/paper/2507.06593/) (2025)
- [LoCAtion: Long-time Collaborative Attention Framework for High Dynamic Range Video Reconstruction](https://mlsanity.com/paper/2603.14377/) (2026)
- [Double-stream registration with pyramid fusion for HDR video with alternating exposures](https://mlsanity.com/paper/2609.31108/) (2026)

## Abstract (arXiv)

High dynamic range (HDR) image synthesis from multiple low dynamic range (LDR) exposures continues to be actively researched. The extension to HDR video synthesis is a topic of significant current interest due to potential cost benefits. For HDR video, a stiff practical challenge presents itself in the form of accurate correspondence estimation of objects between video frames. In particular, loss of data resulting from poor exposures and varying intensity make conventional optical flow methods highly inaccurate. We avoid exact correspondence estimation by proposing a statistical approach via maximum a posterior (MAP) estimation, and under appropriate statistical assumptions and choice of priors and models, we reduce it to an optimization problem of solving for the foreground and background of the target frame. We obtain the background through rank minimization and estimate the foreground via a novel multiscale adaptive kernel regression technique, which implicitly captures local structure and temporal motion by solving an unconstrained optimization problem. Extensive experimental results on both real and synthetic datasets demonstrate that our algorithm is more capable of delivering high-quality HDR videos than current state-of-the-art methods, under both subjective and objective assessments. Furthermore, a thorough complexity analysis reveals that our algorithm achieves better complexity-performance trade-off than conventional methods.