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<p>Low-light face videos suffer from severe noise and detail loss, limiting their use in surveillance and photography applications. To address these challenges, this paper proposes DL-Diff, a novel low-light face video enhancement framework that formulates this task as a conditional video-to-video (V2V) generation problem based on pre-trained Latent Diffusion Models (LDMs). DL-Diff extends pre-trained text-to-video models through three components: a pseudo-3D UNet backbone, a restoration component for spatial detail recovery, and a temporal component for inter-frame consistency. A multi-stage training strategy enables efficient domain adaptation from images to videos. Experiments on DID and SDSD datasets demonstrate that DL-Diff achieves superior performance in both perceptual quality (FID: 41.29, LPIPS: 0.17) and temporal consistency (AB(Var): 25.40, MABD: 0.08), significantly outperforming existing methods. The framework produces high-quality videos with realistic visual effects and no flickering artifacts, particularly excelling in extremely dark scenarios. This work demonstrates the potential of leveraging pre-trained diffusion models for video enhancement tasks.</p>

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Temporally consistent low-light face video enhancement via video-to-video conditional diffusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

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  • Xiaofeng Ding,
  • Kailin He,
  • Huo Sun,
  • Juying Yang

摘要

Low-light face videos suffer from severe noise and detail loss, limiting their use in surveillance and photography applications. To address these challenges, this paper proposes DL-Diff, a novel low-light face video enhancement framework that formulates this task as a conditional video-to-video (V2V) generation problem based on pre-trained Latent Diffusion Models (LDMs). DL-Diff extends pre-trained text-to-video models through three components: a pseudo-3D UNet backbone, a restoration component for spatial detail recovery, and a temporal component for inter-frame consistency. A multi-stage training strategy enables efficient domain adaptation from images to videos. Experiments on DID and SDSD datasets demonstrate that DL-Diff achieves superior performance in both perceptual quality (FID: 41.29, LPIPS: 0.17) and temporal consistency (AB(Var): 25.40, MABD: 0.08), significantly outperforming existing methods. The framework produces high-quality videos with realistic visual effects and no flickering artifacts, particularly excelling in extremely dark scenarios. This work demonstrates the potential of leveraging pre-trained diffusion models for video enhancement tasks.