FLIGHT Mode on: A Feather-Light Network for Low-Light Image Enhancement
Abstract
Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these problems, we propose a novel approach named FLIGHT-Net using a sequence of neural architecture blocks. The first block regulates illumination conditions through pixel-wise scene dependent illumination adjustment. The output image is produced in the output of the second block, which includes channel attention and denoising sub-blocks. Our highly efficient neural network architecture delivers state-of-the-art performance with only 25K parameters. The method’s code, pretrained models and resulting images will be publicly available.
Cite
Text
Ozcan et al. "FLIGHT Mode on: A Feather-Light Network for Low-Light Image Enhancement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00445Markdown
[Ozcan et al. "FLIGHT Mode on: A Feather-Light Network for Low-Light Image Enhancement." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/ozcan2023cvprw-flight/) doi:10.1109/CVPRW59228.2023.00445BibTeX
@inproceedings{ozcan2023cvprw-flight,
title = {{FLIGHT Mode on: A Feather-Light Network for Low-Light Image Enhancement}},
author = {Ozcan, Mustafa and Ergezer, Hamza and Ayazoglu, Mustafa},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {4226-4235},
doi = {10.1109/CVPRW59228.2023.00445},
url = {https://mlanthology.org/cvprw/2023/ozcan2023cvprw-flight/}
}