FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement
It addresses the problem of enhancing images under low-light conditions for applications like photography or surveillance, but appears incremental as it builds on existing neural network approaches.
The paper tackles low-light image enhancement by proposing FLIGHT-Net, a neural network with 25K parameters that achieves state-of-the-art performance.
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.