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

null 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

Dongkwan Kim 127 Dec 28, 2022
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,

CV Lab @ Yonsei University 87 Dec 30, 2022
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

Haotong Qin 59 Dec 17, 2022
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. [

Aviv Shamsian 121 Dec 25, 2022
StyleGAN2 - Official TensorFlow Implementation
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

NVIDIA Research Projects 10.1k Dec 28, 2022
Old Photo Restoration (Official PyTorch Implementation)
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
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

null 46 Nov 9, 2022
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

Djordje Miladinovic 34 Jan 19, 2022
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
GitHub Repository
ML for NLP and Computer Vision.

Sparrow is our open-source ML product. It runs on Skipper MLOps infrastructure.

Katana ML 2 Nov 28, 2021
Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds

Unsupervised 3D Human Mesh Recovery from Noisy Point Clouds Xinxin Zuo, Sen Wang, Minglun Gong, Li Cheng Prerequisites We have tested the code on Ubun

41 Dec 12, 2022
python debugger and anti-vm that checks if you're in a virtual machine or if someones trying to debug your file

Anti-Debug was made by Love ❌ code ✅ 🎉 ・What it checks for ・ Kills tools that can be used to debug your file ・ Exits if ran in vm (supports different

Rdimo 31 Aug 09, 2022
(to be released) [NeurIPS'21] Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs

Higher-Order Transformers Kim J, Oh S, Hong S, Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs, NeurIPS 2021. [arxiv] W

Jinwoo Kim 44 Dec 28, 2022
Fashion Recommender System With Python

Fashion-Recommender-System Thr growing e-commerce industry presents us with a la

Omkar Gawade 2 Feb 02, 2022
Code for "Unsupervised State Representation Learning in Atari"

Unsupervised State Representation Learning in Atari Ankesh Anand*, Evan Racah*, Sherjil Ozair*, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm This

Mila 217 Jan 03, 2023
TVNet: Temporal Voting Network for Action Localization

TVNet: Temporal Voting Network for Action Localization This repo holds the codes of paper: "TVNet: Temporal Voting Network for Action Localization". P

hywang 5 Jul 26, 2022
Code release for Local Light Field Fusion at SIGGRAPH 2019

Local Light Field Fusion Project | Video | Paper Tensorflow implementation for novel view synthesis from sparse input images. Local Light Field Fusion

1.1k Dec 27, 2022
Image-generation-baseline - MUGE Text To Image Generation Baseline

MUGE Text To Image Generation Baseline Requirements and Installation More detail

23 Oct 17, 2022
TICC is a python solver for efficiently segmenting and clustering a multivariate time series

TICC TICC is a python solver for efficiently segmenting and clustering a multivariate time series. It takes as input a T-by-n data matrix, a regulariz

406 Dec 12, 2022
Neural Ensemble Search for Performant and Calibrated Predictions

Neural Ensemble Search Introduction This repo contains the code accompanying the paper: Neural Ensemble Search for Performant and Calibrated Predictio

AutoML-Freiburg-Hannover 26 Dec 12, 2022
TensorFlow for Raspberry Pi

TensorFlow on Raspberry Pi It's officially supported! As of TensorFlow 1.9, Python wheels for TensorFlow are being officially supported. As such, this

Sam Abrahams 2.2k Dec 16, 2022
A very impractical 3D rendering engine that runs in the python terminal.

Terminal-3D-Render A very impractical 3D rendering engine that runs in the python terminal. do NOT try to run this program using the standard python I

23 Dec 31, 2022
Deep motion generator collections

GenMotion GenMotion (/gen’motion/) is a Python library for making skeletal animations. It enables easy dataset loading and experiment sharing for synt

23 May 24, 2022
Reverse engineer your pytorch vision models, in style

🔍 Rover Reverse engineer your CNNs, in style Rover will help you break down your CNN and visualize the features from within the model. No need to wri

Mayukh Deb 32 Sep 24, 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b

61.4k Jan 04, 2023
Few-shot NLP benchmark for unified, rigorous eval

FLEX FLEX is a benchmark and framework for unified, rigorous few-shot NLP evaluation. FLEX enables: First-class NLP support Support for meta-training

AI2 85 Dec 03, 2022
Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU)

DocFormer - PyTorch Implementation of DocFormer: End-to-End Transformer for Document Understanding, a multi-modal transformer based architecture for t

171 Jan 06, 2023
Run object detection model on the Raspberry Pi

Using TensorFlow Lite with Python is great for embedded devices based on Linux, such as Raspberry Pi.

Dimitri Yanovsky 6 Oct 08, 2022
This is a tensorflow-based rotation detection benchmark, also called AlphaRotate.

AlphaRotate: A Rotation Detection Benchmark using TensorFlow Abstract AlphaRotate is maintained by Xue Yang with Shanghai Jiao Tong University supervi

yangxue 972 Jan 05, 2023