Corpus ID: 212737041

Burst Denoising of Dark Images

@article{Karadeniz2020BurstDO,
  title={Burst Denoising of Dark Images},
  author={Ahmet Serdar Karadeniz and Erkut Erdem and Aykut Erdem},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.07823}
}
  • Ahmet Serdar Karadeniz, Erkut Erdem, Aykut Erdem
  • Published in ArXiv 2020
  • Computer Science
  • Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional image enhancement techniques almost impossible to apply. Very recently, researchers have shown promising results using learning based approaches. Motivated by these ideas, in this paper, we propose a deep learning framework for obtaining clean and colorful RGB images from extremely dark raw images. The backbone of our… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 40 REFERENCES

    Learning to See in the Dark

    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL

    Improving Extreme Low-Light Image Denoising via Residual Learning

    VIEW 3 EXCERPTS
    HIGHLY INFLUENTIAL

    Deep Sets

    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    FuseMODNet: Real-Time Camera and LiDAR Based Moving Object Detection for Robust Low-Light Autonomous Driving

    VIEW 1 EXCERPT

    Getting to Know Low-light Images with The Exclusively Dark Dataset

    Getting to know lowlight images with the exclusively dark

    • Yuen Peng Loh, Chee Seng Chan
    • dataset. Comput. Vision Image Understanding,
    • 2019
    VIEW 1 EXCERPT

    Getting to know lowlight images with the exclusively dark dataset. Comput. Vision Image Understanding

    • Peng Yuen, Chee Seng Loh, Chan
    • 2019

    Handheld mobile photography in very low light

    VIEW 3 EXCERPTS