Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Last update: Jan 01, 2023

Related tags

Deep Learning Bread

Overview

Low-light Image Enhancement via Breaking Down the Darkness

by Qiming Hu, Xiaojie Guo.

1. Dependencies

Python3
PyTorch>=1.0
OpenCV-Python, TensorboardX
NVIDIA GPU+CUDA

2. Network Architecture

3. Data Preparation

3.1. Training dataset

485 low/high-light image pairs from our485 of LOL dataset, each low image of which is augmented by our exposure_augment.py to generate 8 images under different exposures.
To train the MECAN (if it is desired), 559 randomly-selected multi-exposure sequences from SICE are adopted.

3.2. Tesing dataset

The images for testing can be downloaded in this link.

4. Usage

4.1. Training

Multi-exposure data synthesis: python exposure_augment.py
Train IAN: python train_IAN.py -m IAN --comment IAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche
Train ANSN: python train_ANSN.py -m1 IAN -m2 ANSN --comment ANSN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche -m1w ./checkpoints/IAN_335.pth
Train CAN: python train_CAN.py -m1 IAN -m3 FuseNet --comment CAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche -m1w ./checkpoints/IAN_335.pth
Train MECAN on SICE: python train_MECAN.py -m FuseNet --comment MECAN_train --batch_size 1 --val_interval 1 --num_epochs 500 --lr 0.001 --no_sche
Finetune MECAN on SICE and LOL datasets: python train_MECAN_finetune.py -m FuseNet --comment MECAN_finetune --batch_size 1 --val_interval 1 --num_epochs 500 --lr 1e-4 --no_sche -mw ./checkpoints/FuseNet_MECAN_for_Finetuning_404.pth

4.2. Testing

[Tips]: Using gamma correction for evaluation with parameter --gc; Show extra intermediate outputs with parameter --save_extra
Evaluation: python eval_Bread.py -m1 IAN -m2 ANSN -m3 FuseNet -m4 FuseNet --mef --comment Bread+NFM+ME[eval] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth -m4w ./checkpoints/FuseNet_NFM_297.pth
Testing: python test_Bread.py -m1 IAN -m2 ANSN -m3 FuseNet -m4 FuseNet --mef --comment Bread+NFM+ME[test] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth -m4w ./checkpoints/FuseNet_NFM_297.pth
Remove NFM: python test_Bread_NoNFM.py -m1 IAN -m2 ANSN -m3 FuseNet --mef -a 0.10 --comment Bread+ME[test] --batch_size 1 -m1w ./checkpoints/IAN_335.pth -m2w ./checkpoints/ANSN_422.pth -m3w ./checkpoints/FuseNet_MECAN_251.pth

4.3. Trained weights

Please refer to our release.

5. Quantitative comparison on eval15

6. Visual comparison on eval15

7. Visual comparison on DICM

8. Visual comparison on VV and MEF-DS

You might also like...

Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

35 Dec 6, 2022

Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

127 Dec 28, 2022

An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

PyTorch implementation of SFNet This is the implementation of the paper "SFNet: Learning Object-aware Semantic Correspondence". For more information,

87 Dec 30, 2022

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

59 Dec 17, 2022

Official code implementation for "Personalized Federated Learning using Hypernetworks"

Personalized Federated Learning using Hypernetworks This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [

121 Dec 25, 2022

StyleGAN2 - Official TensorFlow Implementation

10.1k Dec 28, 2022

Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

11.3k Dec 30, 2022

Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

46 Nov 9, 2022

Official PyTorch implementation of Spatial Dependency Networks.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović Aleksandar Stanić Stefan Bauer Jürgen Schmid

34 Jan 19, 2022

Comments

How to create data?

I have download datasets, but I have no idea about how to creat data. I read the code and found that I need eval/images eval/targets train/images_aug train/targets to train. Could you please tell me how to perpare these for folder? thanks so much!

opened by Adolfhill 4

Official implementation for "Low-light Image Enhancement via Breaking Down the Darkness"

Related tags

Overview

Low-light Image Enhancement via Breaking Down the Darkness

1. Dependencies

2. Network Architecture

3. Data Preparation

3.1. Training dataset

3.2. Tesing dataset

4. Usage

4.1. Training

4.2. Testing

4.3. Trained weights

5. Quantitative comparison on eval15

6. Visual comparison on eval15

7. Visual comparison on DICM

8. Visual comparison on VV and MEF-DS

You might also like...

Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

Official code implementation for "Personalized Federated Learning using Hypernetworks"

StyleGAN2 - Official TensorFlow Implementation

Old Photo Restoration (Official PyTorch Implementation)

Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

Official PyTorch implementation of Spatial Dependency Networks.

Comments

How to create data?

Releases(checkpoints)

checkpoints(Nov 30, 2021)

Owner

Qiming Hu

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)

A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

QMagFace: Simple and Accurate Quality-Aware Face Recognition

This repo contains the code for paper Inverse Weighted Survival Games

My course projects for the 2021 Spring Machine Learning course at the National Taiwan University (NTU)

🔅 Shapash makes Machine Learning models transparent and understandable by everyone

The repo for reproducing Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study

Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

Asymmetric metric learning for knowledge transfer

Learning from Synthetic Humans, CVPR 2017

DataCLUE: 国内首个以数据为中心的AI测评（含模型分析报告）

Use graph-based analysis to re-classify stocks and to improve Markowitz portfolio optimization

A benchmark framework for Tensorflow

This project is the PyTorch implementation of our CVPR 2022 paper:

Implementations of the algorithms in the paper Approximative Algorithms for Multi-Marginal Optimal Transport and Free-Support Wasserstein Barycenters

DeiT: Data-efficient Image Transformers

Immortal tracker