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.
@article{arxiv.2305.10889,
title = {FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement},
author = {Mustafa Ozcan and Hamza Ergezer and Mustafa Ayazaoglu},
journal= {arXiv preprint arXiv:2305.10889},
year = {2023}
}