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Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond

ArXiv

Title

Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
Shen Zheng, Yiling Ma, Jinqian Pan, Changjie Lu, Gaurav Gupta

Updates

  • 2024/1/1: We update the arXiv version with important revisions.
  • 2023/4/16: The enhanced images and the metric scripts have been uploaded.
  • 2023/3/24: The Night Wenzhou Dataset has been uploaded.
  • 2023/2/8: The arXiv has been updated. The current version contains 21 pages, 9 tables, and 25 figures!

Highlights

  • Present a comprehensive survey of low-light image and videeo enhancement (LLIE).
  • Propose SICE_Grad and SICE_Mix image datasets to represent complex mixed over-/under-exposed scenes.
  • Introduce Night Wenzhou video dataset that features fast-moving aerial scenes and streetscapes with varied illuminations and degradations.

Timeline

Taxonomy

Category

Traditional Learning Deep Learning

Our Datasets

SICE_Grad and SICE_Mix [Download] Night Wenzhou [Download]

Models (in chronological order)

  • PIE (TIP 2015) [paper] [Python]

    • A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation
  • LIME (TIP 2016) [paper] [Python]

    • LIME: Low-Light Image Enhancement via Illumination Map Estimation
  • LLNet (PR 2017) [paper] [Theano]

    • LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
  • MBLLEN (BMVC 2017) [paper] [Keras]

    • MBLLEN: Low-light Image/Video Enhancement Using CNNs
  • LightenNet (PRL 2018) [paper] [MATLAB]

    • LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement
  • Retinex-Net (BMVC 2018) [paper] [TensorFlow]

    • Deep Retinex Decomposition for Low-Light Enhancement
  • SID (CVPR 2018) [paper] [TensorFlow]

    • Learning to See in the Dark
  • DeepUPE (CVPR 2019) [paper] [TensorFlow]

    • Underexposed Photo Enhancement using Deep Illumination Estimation
  • EEMEFN (AAAI 2019) [paper] [TensorFlow]

    • EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network
  • ExCNet (ACMMM 2019) [paper] [Tensorflow ]

    • Zero-shot restoration of back-lit images using deep internal learning
  • KinD (ACMMM 2019) [paper] [TensorFlow]

    • Kindling the darkness: A practical low-light image enhancer
  • Zero-DCE (CVPR 2020) [paper] [PyTorch]

    • Zero-reference deep curve estimation for low-light image enhancement
  • DRBN (CVPR 2020) [paper] [PyTorch]

    • From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement
  • Xu et al. (CVPR 2020) [paper] [PyTorch]

    • Learning to Restore Low-Light Images via Decomposition-and-Enhancement
  • DLN (TIP 2020) [paper] [PyTorch]

    • Lightening network for low-light image enhancement
  • DeepLPF (CVPR 2020) [paper] [PyTorch]

    • Deep Local Parametric Filters for Image Enhancement
  • EnlightenGAN (TIP 2021) [paper] [PyTorch]

    • EnlightenGAN: Deep Light Enhancement without Paired Supervision
  • KinD++ (IJCV 2021) [paper] [TensorFlow]

    • Beyond Brightening Low-light Images
  • Zero-DCE++ (TPAMI 2021) [paper] [PyTorch]

    • Learning to enhance low-light image via zero-reference deep curve estimation
  • Zhang et al. (CVPR 2021) [paper] [PyTorch]

    • Learning Temporal Consistency for Low Light Video Enhancement from Single Images
  • RUAS (CVPR 2021) [paper] [PyTorch]

    • Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
  • UTVNet (ICCV 2021) [paper] [PyTorch]

    • UTVNet: Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement
  • SDSD (ICCV 2021) [paper] [PyTorch]

    • Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment
  • RetinexDIP (TCSVT 2021) [paper] [PyTorch]

    • RetinexDIP: A Unified Deep Framework for Low-light Image Enhancement
  • SGZ (WACV 2022) [paper] [PyTorch]

    • Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
  • LLFlow (AAAI 2022) [paper] [PyTorch]

    • Low-Light Image Enhancement with Normalizing Flow
  • SNR-Aware (CVPR 2022) [paper] [PyTorch]

    • SNR-Aware Low-light Image Enhancement
  • SCI (CVPR 2022) [paper] [PyTorch]

    • Toward Fast, Flexible, and Robust Low-Light Image Enhancement
  • URetinex-Net (CVPR 2022) [paper] [PyTorch]

    • URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement
  • Dong et al. (CVPR 2022) [paper] [PyTorch]

    • Abandoning the Bayer-Filter to See in the Dark
  • MAXIM (CVPR 2022) [paper] [Jax]

    • MAXIM: Multi-Axis MLP for Image Processing
  • BIPNet (CVPR 2022) [paper] [PyTorch]

    • Burst Image Restoration and Enhancement
  • LCDPNet (ECCV 2022) [paper] [PyTorch]

    • Local Color Distributions Prior for Image Enhancement
  • IAT (BMVC 2022) [paper] [PyTorch]

    • You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

Benchmark Datasets

Enhanced Images

  • Enhanced images for all baselines are here

Metrics

Full-Reference

Non-Reference

Subjective

  • User Study

Efficiency

Surveys

  • IEEE [paper]

    • An experiment-based review of low-light image enhancement methods
  • IJCV 2021 [paper]

    • Benchmarking low-light image enhancement and beyond
  • TPAMI 2021 [paper]

    • Low-Light Image and Video Enhancement Using Deep Learning: A Survey

Related Repositories

BibTeX

If you find this repository helpful, please cite our paper.

@article{zheng2022low,
  title={Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond},
  author={Zheng, Shen and Ma, Yiling and Pan, Jinqian and Lu, Changjie and Gupta, Gaurav},
  journal={arXiv preprint arXiv:2212.10772},
  year={2022}
}

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