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
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning

AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning AutoPentest-DRL is an automated penetration testing framework based o

Cyber Range Organization and Design Chair 217 Jan 01, 2023
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo

TuZheng 20 Sep 07, 2022
Implementation of MA-Trace - a general-purpose multi-agent RL algorithm for cooperative environments.

Off-Policy Correction For Multi-Agent Reinforcement Learning This repository is the official implementation of Off-Policy Correction For Multi-Agent R

4 Aug 18, 2022
As-ViT: Auto-scaling Vision Transformers without Training

As-ViT: Auto-scaling Vision Transformers without Training [PDF] Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou In ICLR 2

VITA 68 Sep 05, 2022
Soomvaar is the repo which 🏩 contains different collection of 👨‍💻🚀code in Python and 💫✨Machine 👬🏼 learning algorithms📗📕 that is made during 📃 my practice and learning of ML and Python✨💥

Soomvaar 📌 Introduction Soomvaar is the collection of various codes implement in machine learning and machine learning algorithms with python on coll

Felix-Ayush 42 Dec 30, 2022
Auditing Black-Box Prediction Models for Data Minimization Compliance

Data-Minimization-Auditor An auditing tool for model-instability based data minimization that is introduced in "Auditing Black-Box Prediction Models f

Bashir Rastegarpanah 2 Mar 24, 2022
Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020

XDVioDet Official implementation of "Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision" ECCV2020. The proj

peng 64 Dec 12, 2022
Repository for "Space-Time Correspondence as a Contrastive Random Walk" (NeurIPS 2020)

Space-Time Correspondence as a Contrastive Random Walk This is the repository for Space-Time Correspondence as a Contrastive Random Walk, published at

A. Jabri 239 Dec 27, 2022
An executor that loads ONNX models and embeds documents using the ONNX runtime.

ONNXEncoder An executor that loads ONNX models and embeds documents using the ONNX runtime. Usage via Docker image (recommended) from jina import Flow

Jina AI 2 Mar 15, 2022
CryptoFrog - My First Strategy for freqtrade

cryptofrog-strategies CryptoFrog - My First Strategy for freqtrade NB: (2021-04-20) You'll need the latest freqtrade develop branch otherwise you migh

Robert Davey 137 Jan 01, 2023
An OpenAI-Gym Package for Training and Testing Reinforcement Learning algorithms with OpenSim Models

Authors: Utkarsh A. Mishra and Dr. Dimitar Stanev Advisors: Dr. Dimitar Stanev and Prof. Auke Ijspeert, Biorobotics Laboratory (BioRob), EPFL Video Pl

Utkarsh Mishra 16 Dec 13, 2022
Dynamic Head: Unifying Object Detection Heads with Attentions

Dynamic Head: Unifying Object Detection Heads with Attentions dyhead_video.mp4 This is the official implementation of CVPR 2021 paper "Dynamic Head: U

Microsoft 550 Dec 21, 2022
PyTorch implementation DRO: Deep Recurrent Optimizer for Structure-from-Motion

DRO: Deep Recurrent Optimizer for Structure-from-Motion This is the official PyTorch implementation code for DRO-sfm. For technical details, please re

Alibaba Cloud 56 Dec 12, 2022
Simulation of self-focusing of laser beams in condensed media

What is it? Program for scientific research, which allows to simulate the phenomenon of self-focusing of different laser beams (including Gaussian, ri

Evgeny Vasilyev 13 Dec 24, 2022
Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Code and data to accompany the camera-ready version of "Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation" in EMNLP 2021

Mozhdeh Gheini 16 Jul 16, 2022
Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection

Rotated Box Is Back : Accurate Box Proposal Network for Scene Text Detection This material is supplementray code for paper accepted in ICDAR 2021 We h

NCSOFT 30 Dec 21, 2022
A lightweight deep network for fast and accurate optical flow estimation.

FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation The official PyTorch implementation of FastFlowNet (ICRA 2021). Authors: Lingtong

Tone 161 Jan 03, 2023
code for ICCV 2021 paper 'Generalized Source-free Domain Adaptation'

G-SFDA Code (based on pytorch 1.3) for our ICCV 2021 paper 'Generalized Source-free Domain Adaptation'. [project] [paper]. Dataset preparing Download

Shiqi Yang 84 Dec 26, 2022
Code and Data for NeurIPS2021 Paper "A Dataset for Answering Time-Sensitive Questions"

Time-Sensitive-QA The repo contains the dataset and code for NeurIPS2021 (dataset track) paper Time-Sensitive Question Answering dataset. The dataset

wenhu chen 35 Nov 14, 2022