Diffusion-Promoted HDR Video Reconstruction
Yuanshen Guan, Ruikang Xu, Mingde Yao, Ruisheng Gao, Lizhi Wang,, Zhiwei Xiong

TL;DR
This paper introduces HDR-V-Diff, a diffusion-based method for HDR video reconstruction that captures realistic details and reduces artifacts by combining a latent diffusion model with temporal alignment and cross-attention mechanisms.
Contribution
The paper proposes a novel diffusion-promoted HDR video reconstruction framework with a latent diffusion model, temporal alignment, and cross-attention, improving quality and efficiency over existing methods.
Findings
Achieves state-of-the-art results on multiple datasets.
Effectively reduces ghosting artifacts and preserves details.
Incorporates a novel exposure embedding and zero-init cross-attention mechanism.
Abstract
High dynamic range (HDR) video reconstruction aims to generate HDR videos from low dynamic range (LDR) frames captured with alternating exposures. Most existing works solely rely on the regression-based paradigm, leading to adverse effects such as ghosting artifacts and missing details in saturated regions. In this paper, we propose a diffusion-promoted method for HDR video reconstruction, termed HDR-V-Diff, which incorporates a diffusion model to capture the HDR distribution. As such, HDR-V-Diff can reconstruct HDR videos with realistic details while alleviating ghosting artifacts. However, the direct introduction of video diffusion models would impose massive computational burden. Instead, to alleviate this burden, we first propose an HDR Latent Diffusion Model (HDR-LDM) to learn the distribution prior of single HDR frames. Specifically, HDR-LDM incorporates a tonemapping strategy to…
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Taxonomy
TopicsImage Enhancement Techniques · Image and Signal Denoising Methods · Advanced Image Processing Techniques
MethodsDiffusion · Latent Diffusion Model
