Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Overview

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

This repository provides a unified online platform, LoLi-Platform http://mc.nankai.edu.cn/ll/, that covers many popular deep learning-based LLIE methods, of which the results can be produced through a user-friendly web interface, contains a low-light image and video dataset, LoLi-Phone (will be released soon), in which the images and videos are taken by various phones' cameras under diverse illumination conditions and scenes, and collects deep learning-based low-light image and video enhancement methods, datasets, and evaluation metrics. More content and details can be found in our Survey Paper: Lighting the Darkness in the Deep Learning Era. We provide the comparison results on the real low-light videos taken by different mobile phones’ cameras at YouTube https://www.youtube.com/watch?v=Elo9TkrG5Oo&t=6s.

We will periodically update the content. Welcome to let us know if we miss your work that is published in top-tier Journal or conference. We will add it.

Our LoLi-Platform supports the function of download. Please right click and then save the figure.

If you use this dataset or platform, please cite our paper. Please hit the star at the top-right corner. Thanks!

Contents

  1. LoLi-Platform
  2. LoLi-Phone Dataset
  3. Methods
  4. Datasets
  5. Metrics
  6. Citation

LoLi-Platform

Currently, the LoLi-Platform covers 13 popular deep learning-based LLIE methods including LLNet, LightenNet, Retinex-Net, EnlightenGAN, MBLLEN, KinD, KinD++, TBEFN, DSLR, DRBN, ExCNet, Zero-DCE, and RRDNet, where the results of any inputs can be produced through a user-friendly web interface. Have fun: LoLi-Platform.

LoLi-Phone

Overview LoLi-Phone dataset contains 120 videos (55,148 images) taken by 18 different phones' cameras including iPhone 6s, iPhone 7, iPhone7 Plus, iPhone8 Plus, iPhone 11, iPhone 11 Pro, iPhone XS, iPhone XR, iPhone SE, Xiaomi Mi 9, Xiaomi Mi Mix 3, Pixel 3, Pixel 4, Oppo R17, Vivo Nex, LG M322, OnePlus 5T, Huawei Mate 20 Pro under diverse illumination conditions (e.g., weak illumination, underexposure, dark, extremely dark, back-lit, non-uniform light, color light sources, etc.) in the indoor and outdoor scenes. Anyone can access the LoLi-Phone dataset.

Methods

Overview

Date Publication Title Abbreviation Code Platform
2017 PR LLNet: A deep autoencoder approach to natural low-light image enhancement paper LLNet Code Theano
2018 PRL LightenNet: A convolutional neural network for weakly illuminated image enhancement paper LightenNet Code Caffe & MATLAB
2018 BMVC Deep retinex decomposition for low-light enhancement paper Retinex-Net Code TensorFlow
2018 BMVC MBLLEN: Low-light image/video enhancement using CNNs paper MBLLEN Code TensorFlow
2018 TIP Learning a deep single image contrast enhancer from multi-exposure images paper SCIE Code Caffe & MATLAB
2018 CVPR Learning to see in the dark paper Chen et al. Code TensorFlow
2018 NeurIPS DeepExposure: Learning to expose photos with asynchronously reinforced adversarial learning paper DeepExposure TensorFlow
2019 ICCV Seeing motion in the dark paper Chen et al. Code TensorFlow
2019 ICCV Learning to see moving object in the dark paper Jiang and Zheng Code TensorFlow
2019 CVPR Underexposed photo enhancement using deep illumination estimation paper DeepUPE Code TensorFlow
2019 ACMMM Kindling the darkness: A practical low-light image enhancer paper KinD Code TensorFlow
2019 ACMMM (IJCV) Kindling the darkness: A practical low-light image enhancer paper (Beyond brightening low-light images paper) KinD (KinD++) Code TensorFlow
2019 ACMMM Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement paper Wang et al. Caffe
2019 TIP Low-light image enhancement via a deep hybrid network paper Ren et al. Caffe
2019(2021) arXiv(TIP) EnlightenGAN: Deep light enhancement without paired supervision paper arxiv EnlightenGAN Code PyTorch
2019 ACMMM Zero-shot restoration of back-lit images using deep internal learning paper ExCNet Code PyTorch
2020 CVPR Zero-reference deep curve estimation for low-light image enhancement paper Zero-DCE Code PyTorch
2020 CVPR From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement paper DRBN Code PyTorch
2020 ACMMM Fast enhancement for non-uniform illumination images using light-weight CNNs paper Lv et al. TensorFlow
2020 ACMMM Integrating semantic segmentation and retinex model for low light image enhancement paper Fan et al.
2020 CVPR Learning to restore low-light images via decomposition-and-enhancement paper Xu et al. PyTorch
2020 AAAI EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network paper EEMEFN PyTorch
2020 TIP Lightening network for low-light image enhancement paper DLN PyTorch
2020 TMM Luminance-aware pyramid network for low-light image enhancement paper LPNet PyTorch
2020 ECCV Low light video enhancement using synthetic data produced with an intermediate domain mapping paper SIDGAN TensorFlow
2020 TMM TBEFN: A two-branch exposure-fusion network for low-light image enhancement paper TBEFN Code TensorFlow
2020 ICME Zero-shot restoration of underexposed images via robust retinex decomposition paper RRDNet Code PyTorch
2020 TMM DSLR: Deep stacked laplacian restorer for low-light image enhancement paper DSLR Code PyTorch

Datasets

Abbreviation Number Format Real/Synetic Video Paired/Unpaired/Application Dataset
LOL paper 500 RGB Real No Paired Dataset
SCIE paper 4413 RGB Real No Paired Dataset
MIT-Adobe FiveK paper 5000 Raw Real No Paired Dataset
SID paper 5094 Raw Real No Paired Dataset
DRV paper 202 Raw Real Yes Paired Dataset
SMOID paper 179 Raw Real Yes Paired Dataset
LIME paper 10 RGB Real No Unpaired Dataset
NPE paper 84 RGB Real No Unpaired Dataset
MEF paper 17 RGB Real No Unpaired Dataset
DICM paper 64 RGB Real No Unpaired Dataset
VV 24 RGB Real No Unpaired Dataset
ExDARK paper 7363 RGB Real No Application Dataset
BBD-100K paper 10,000 RGB Real Yes Application Dataset
DARK FACE paper 6000 RGB Real No Application Dataset

Metrics

Abbreviation Full-/Non-Reference Platform Code
MAE (Mean Absolute Error) Full-Reference
MSE (Mean Square Error) Full-Reference
PSNR (Peak Signal-to-Noise Ratio) Full-Reference
SSIM (Structural Similarity Index Measurement) Full-Reference MATLAB Code
LPIPS (Learned Perceptual Image Patch Similarity) Full-Reference PyTorch Code
LOE (Lightness Order Error) Non-Reference MATLAB Code
NIQE (Naturalness Image Quality Evaluator) Non-Reference MATLAB Code
PI (Perceptual Index) Non-Reference MATLAB Code
SPAQ (Smartphone Photography Attribute and Quality) Non-Reference PyTorch Code
NIMA (Neural Image Assessment) Non-Reference PyTorch/TensorFlow Code/Code

Citation

If you find the repository helpful in your resarch, please cite the following paper.

@article{LoLi,
  title={Lighting the Darkness in the Deep Learning Era},
  author={Li, Chongyi and Guo, Chunle and Han, Linghao and Jiang, Jun and Cheng, Ming-Ming and Gu, Jinwei and Loy, Chen Change},
  journal={arXiv:2104.10729},
  year={2021}
}

Contact Information

[email protected]

[email protected]
Owner
Chongyi Li
Chongyi Li
Python package provinding tools for artistic interactive applications using AI

Documentation redrawing Python package provinding tools for artistic interactive applications using AI Created by ReDrawing Campinas team for the Open

ReDrawing Campinas 1 Sep 30, 2021
Whisper is a file-based time-series database format for Graphite.

Whisper Overview Whisper is one of three components within the Graphite project: Graphite-Web, a Django-based web application that renders graphs and

Graphite Project 1.2k Dec 25, 2022
Benchmark for Answering Existential First Order Queries with Single Free Variable

EFO-1-QA Benchmark for First Order Query Estimation on Knowledge Graphs This repository contains an entire pipeline for the EFO-1-QA benchmark. EFO-1

HKUST-KnowComp 14 Oct 24, 2022
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems

Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems This is our experimental code for RecSys 2021 paper "Learning

11 Jul 28, 2022
Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Accompanying code for the paper Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Kevin Wilkinghoff 6 Dec 01, 2022
PyTorch implementation HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

HoroPCA This code is the official PyTorch implementation of the ICML 2021 paper: HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projec

HazyResearch 52 Nov 14, 2022
The implementation of the algorithm in the paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020.

DS3L This is the code for paper "Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data" published in ICML 2020. Setups The code is implem

Guolz 36 Oct 19, 2022
Transfer Learning Remote Sensing

Transfer_Learning_Remote_Sensing Simulation R codes for data generation and visualizations are in the folder simulation. Experiment: California Housin

2 Jun 21, 2022
a generic C++ library for image analysis

VIGRA Computer Vision Library Copyright 1998-2013 by Ullrich Koethe This file is part of the VIGRA computer vision library. You may use,

Ullrich Koethe 378 Dec 30, 2022
A generalist algorithm for cell and nucleus segmentation.

Cellpose | A generalist algorithm for cell and nucleus segmentation. Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cel

MouseLand 733 Dec 29, 2022
Reimplementation of the paper "Attention, Learn to Solve Routing Problems!" in jax/flax.

JAX + Attention Learn To Solve Routing Problems Reinplementation of the paper Attention, Learn to Solve Routing Problems! using Jax and Flax. Fully su

Gabriela Surita 7 Dec 01, 2022
SANet: A Slice-Aware Network for Pulmonary Nodule Detection

SANet: A Slice-Aware Network for Pulmonary Nodule Detection This paper (SANet) has been accepted and early accessed in IEEE TPAMI 2021. This code and

Jie Mei 39 Dec 17, 2022
本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。

说明 本项目是一个带有前端界面的垃圾分类项目,加载了训练好的模型参数,模型为efficientnetb4,暂时为40分类问题。 python依赖 tf2.3 、cv2、numpy、pyqt5 pyqt5安装 pip install PyQt5 pip install PyQt5-tools 使用 程

4 May 04, 2022
[ICCV'2021] "SSH: A Self-Supervised Framework for Image Harmonization", Yifan Jiang, He Zhang, Jianming Zhang, Yilin Wang, Zhe Lin, Kalyan Sunkavalli, Simon Chen, Sohrab Amirghodsi, Sarah Kong, Zhangyang Wang

SSH: A Self-Supervised Framework for Image Harmonization (ICCV 2021) code for SSH Representative Examples Main Pipeline RealHM DataSet Google Drive Pr

VITA 86 Dec 02, 2022
Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020)

Learning to Simulate Dynamic Environments with GameGAN PyTorch code for GameGAN Learning to Simulate Dynamic Environments with GameGAN Seung Wook Kim,

199 Dec 26, 2022
OpenCVのGrabCut()を利用したセマンティックセグメンテーション向けアノテーションツール(Annotation tool using GrabCut() of OpenCV. It can be used to create datasets for semantic segmentation.)

[Japanese/English] GrabCut-Annotation-Tool GrabCut-Annotation-Tool.mp4 OpenCVのGrabCut()を利用したアノテーションツールです。 セマンティックセグメンテーション向けのデータセット作成にご使用いただけます。 ※Grab

KazuhitoTakahashi 30 Nov 18, 2022
Pytorch implementation for the EMNLP 2020 (Findings) paper: Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

Path-Generator-QA This is a Pytorch implementation for the EMNLP 2020 (Findings) paper: Connecting the Dots: A Knowledgeable Path Generator for Common

Peifeng Wang 33 Dec 05, 2022
Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral) [Project] [Paper] [Demo] [Related Work: A2RL (for Auto Image Cropping)] [C

Wu Huikai 402 Dec 27, 2022
PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Yoonki Jeong 129 Dec 22, 2022
CRNN With PyTorch

CRNN-PyTorch Implementation of https://arxiv.org/abs/1507.05717

Vadim 4 Sep 01, 2022