Low-Light Image Enhancement

120 papers with code • 21 benchmarks • 21 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
2 papers

Most implemented papers

SSD: Single Shot MultiBox Detector

weiliu89/caffe 8 Dec 2015

Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference.

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?

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.

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

caiyuanhao1998/retinexformer ICCV 2023

When enhancing low-light images, many deep learning algorithms are based on the Retinex theory.

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.

Test-Time Training with Self-Supervision for Generalization under Distribution Shifts

yueatsprograms/ttt_cifar_release 29 Sep 2019

In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions.

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.