Kindling the Darkness: A Practical Low-light Image Enhancer

TL;DR

This paper introduces KinD, a low-light image enhancement network inspired by Retinex theory, which decomposes images into illumination and reflectance components to improve visibility while reducing artifacts.

Contribution

The work presents a simple yet effective network trained with paired images, outperforming state-of-the-art methods in low-light enhancement and enabling real-time processing.

Findings

Outperforms existing methods in low-light enhancement

Robust against severe visual defects

Processes images in under 50ms on a GPU

Abstract

Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradations, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning up the brightness of dark regions will inevitably amplify hidden artifacts. This work builds a simple yet effective network for \textbf{Kin}dling the \textbf{D}arkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components. One component (illumination) is responsible for light adjustment, while the other (reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting to be better regularized/learned. It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information. Extensive experiments are conducted to demonstrate the efficacy of our design and its superiority over state-of-the-art alternatives. Our KinD is robust against severe visual defects, and user-friendly to arbitrarily adjust light levels. In addition, our model spends less than 50ms to process an image in VGA resolution on a 2080Ti GPU. All the above merits make our KinD attractive for practical use.