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

Related tags

Deep LearningBread
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

figure_arch

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

table_eval

6. Visual comparison on eval15

figure_eval

7. Visual comparison on DICM

figure_test_dicm

8. Visual comparison on VV and MEF-DS

figure_test_vv_mefds

You might also like...
Official implementation of our paper
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

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

An official implementation of
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,

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

Official code implementation for
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. [

StyleGAN2 - Official TensorFlow Implementation
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

 Old Photo Restoration (Official PyTorch Implementation)
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Official implementation of
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

Official PyTorch implementation of Spatial Dependency Networks.
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

Comments
  • How to create data?

    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
Owner
Qiming Hu
Qiming Hu
Implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

CrossViT : Cross-Attention Multi-Scale Vision Transformer for Image Classification This is an unofficial PyTorch implementation of CrossViT: Cross-Att

Rishikesh (ऋषिकेश) 103 Nov 25, 2022
code for paper -- "Seamless Satellite-image Synthesis"

Seamless Satellite-image Synthesis by Jialin Zhu and Tom Kelly. Project site. The code of our models borrows heavily from the BicycleGAN repository an

Light 14 Apr 05, 2022
Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper] [Code] The official code repository for our CVPR 2021 paper "Diffusion Probabilis

Shitong Luo 323 Jan 05, 2023
Emotional conditioned music generation using transformer-based model.

This is the official repository of EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has b

hung anna 96 Nov 09, 2022
LIVECell - A large-scale dataset for label-free live cell segmentation

LIVECell dataset This document contains instructions of how to access the data associated with the submitted manuscript "LIVECell - A large-scale data

Sartorius Corporate Research 112 Jan 07, 2023
Pytorch Implementation for Dilated Continuous Random Field

DilatedCRF Pytorch implementation for fully-learnable DilatedCRF. If you find my work helpful, please consider our paper: @article{Mo2022dilatedcrf,

DunnoCoding_Plus 3 Nov 13, 2022
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

What's New Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here. [Oct 2021]: V

Meta Research 2.9k Jan 07, 2023
PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Transformer in PyTorch This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This pape

spencerbraun 160 Dec 19, 2022
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that gene

Guan-Horng Liu 43 Jan 03, 2023
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
Video Instance Segmentation with a Propose-Reduce Paradigm (ICCV 2021)

Propose-Reduce VIS This repo contains the official implementation for the paper: Video Instance Segmentation with a Propose-Reduce Paradigm Huaijia Li

DV Lab 39 Nov 23, 2022
A curated (most recent) list of resources for Learning with Noisy Labels

A curated (most recent) list of resources for Learning with Noisy Labels

Jiaheng Wei 321 Jan 09, 2023
source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval This repository contains source code and pre-trained/fine-tun

Siqi 65 Dec 26, 2022
Semantic Segmentation in Pytorch

PyTorch Semantic Segmentation Introduction This repository is a PyTorch implementation for semantic segmentation / scene parsing. The code is easy to

Hengshuang Zhao 1.2k Jan 01, 2023
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Official PyTorch implementation of UACANet: Uncertainty Aware Context Attention for Polyp Segmentation

UACANet: Uncertainty Aware Context Attention for Polyp Segmentation Official pytorch implementation of UACANet: Uncertainty Aware Context Attention fo

Taehun Kim 85 Dec 14, 2022
Keras Image Embeddings using Contrastive Loss

Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.

Shravan Anand K 5 Mar 21, 2022
Semi-supervised Learning for Sentiment Analysis

Neural-Semi-supervised-Learning-for-Text-Classification-Under-Large-Scale-Pretraining Code, models and Datasets for《Neural Semi-supervised Learning fo

47 Jan 01, 2023
Official PyTorch Implementation of Learning Architectures for Binary Networks

Learning Architectures for Binary Networks An Pytorch Implementation of the paper Learning Architectures for Binary Networks (BNAS) (ECCV 2020) If you

Computer Vision Lab. @ GIST 25 Jun 09, 2022
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022