  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.
