Low-Light Image Enhancement

68 papers with code • 9 benchmarks • 9 datasets

Low-Light Image Enhancement is a computer vision task that involves improving the quality of images captured under low-light conditions. The goal of low-light image enhancement is to make images brighter, clearer, and more visually appealing, without introducing too much noise or distortion.


Use these libraries to find Low-Light Image Enhancement models and implementations

Most implemented papers

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

Li-Chongyi/Zero-DCE CVPR 2020

The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.

EnlightenGAN: Deep Light Enhancement without Paired Supervision

yueruchen/EnlightenGAN 17 Jun 2019

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Kindling the Darkness: A Practical Low-light Image Enhancer

zhangyhuaee/KinD 4 May 2019

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.

Deep Retinex Decomposition for Low-Light Enhancement

weichen582/RetinexNet 14 Aug 2018

Based on the decomposition, subsequent lightness enhancement is conducted on illumination by an enhancement network called Enhance-Net, and for joint denoising there is a denoising operation on reflectance.

Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset

yu-li/AGLLNet 2 Aug 2019

Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark.

Low-Light Image and Video Enhancement Using Deep Learning: A Survey

Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open 21 Apr 2021

Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination.

LIME: Low-light Image Enhancement via Illumination Map Estimation

pvnieo/Low-light-Image-Enhancement IEEE TIP 2016

When one captures images in low-light conditions, the images often suffer from low visibility.

Getting to Know Low-light Images with The Exclusively Dark Dataset

cs-chan/Exclusively-Dark-Image-Dataset 29 May 2018

Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i. e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations.

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

kglore/llnet_color 12 Nov 2015

In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation.

STAR: A Structure and Texture Aware Retinex Model

csjunxu/STAR 16 Jun 2019

A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image.