FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement

Mustafa Ozcan, Hamza Ergezer, Mustafa Ayazaoglu
5/18/2023
cs.CV

No official code found. Best implementation: aselsan-research-imaging-team/flight-net (12 stars, last commit 2023-06-13).

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.

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Code Implementations(1)

FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement (UC2+ @ CVPR 2023)

122PythonJun 8, 20233 years agoNOASSERTION

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Cite this paper

@article{ozcan2023flight,
  title  = {FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement},
  author = {Mustafa Ozcan and Hamza Ergezer and Mustafa Ayazaoglu},
  year   = {2023},
  eprint = {2305.10889},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.10889v1}
}

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