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

Plenoxels: Radiance Fields without Neural Networks

Realtime segmentation with ENet, the fast and accurate segmentation net.

Pgn2tex - Scripts to convert pgn files to latex document. Useful to build books or pdf from pgn studies

Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

Synthesizing and manipulating 2048x1024 images with conditional GANs

Contains supplementary materials for reproduce results in HMC divergence time estimation manuscript

Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

Back to Basics: Efficient Network Compression via IMP

Pytorch implementation of SenFormer: Efficient Self-Ensemble Framework for Semantic Segmentation

Differentiable Annealed Importance Sampling (DAIS)

HandFoldingNet ✌️ : A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton

Lightweight Python library for adding real-time object tracking to any detector.

Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

A script depending on VASP output for calculating Fermi-Softness.

An open-source online reverse dictionary.

Official code for Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"