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

TL;DR

FLIGHT-Net is a lightweight neural network designed for low-light image enhancement, effectively improving image quality with only 25K parameters by addressing illumination, noise, and color inconsistencies.

Contribution

The paper introduces FLIGHT-Net, a novel, highly efficient neural network architecture that achieves state-of-the-art low-light image enhancement with significantly fewer parameters.

Findings

State-of-the-art performance on LLIE benchmarks

Only 25K parameters needed for effective enhancement

Code and models will be publicly available

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.