Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

Last update: Dec 24, 2022

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Overview

R2RNet

Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network." Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu Zou, Fang Lin, and Songchen Han(Submitted to IEEE transaction on Image Processing)

Paper link: https://arxiv.org/abs/2106.14501

Network Architecture

Pytorch

This is a Pytorch implementation of R2RNet.

Requirements

Python 3.x
Pytorch 1.x.0

Dataset

You can download the LSRW dataset from: https://pan.baidu.com/s/1UxFllrtRSh4E8ir8LdTb9w (code: wmr1)

If you use our code and dataset, please cite our paper.

Pre-trained model

The pre-trained models can be download from: https://pan.baidu.com/s/1emGK8_JHNktoEn0dj5OnhQ (code: wmrr)

You shold download the VGG model (https://pan.baidu.com/s/1Rn2NwHt9eZgfg6hQP-DrlQ code:wmr1)and put it into ./model.

Testing Usage

python predict.py

Training Usage

python trian.py

Reference

Code borrows heavily from https://github.com/aasharma90/RetinexNet_PyTorch.

Owner

GitHub Repository

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