JacklE0niden/DM2F_LR

$\boldsymbol{\mathrm{DM2F^{+}-GAN}}$

Rui Lin(Github:JacklE0niden)

baseline:DM2F By Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng.

This repo is the implementation of "Deep Multi-Model Fusion for Single-Image Dehazing" (ICCV 2019), written by Zijun Deng at the South China University of Technology.

Results

Some basic dehazing results can be found at Report.md

• Download model weight files：https://pan.baidu.com/s/1gnxMVDx2S6QHSJdwG9FX0Q?pwd=8nfj

Installation & Preparation

Make sure you have Python>=3.7 installed on your machine.

Environment setup:

1. Create conda environment

   conda create -n dm2f
   conda activate dm2f
   

2. Install dependencies (test with PyTorch 1.8.0):

   1. Install pytorch==1.8.0 torchvision==0.9.0 (via conda, recommend).

   2. Install other dependencies

      pip install -r requirements.txt
      

• Prepare the dataset

  • Download the RESIDE dataset from the official webpage.

  • Download the O-Haze dataset from the official webpage.

  • Make a directory ./data and create a symbolic link for uncompressed data, e.g., ./data/RESIDE.

Preprocess data

1. Run by python tools/preprocess_ohaze_data.py

Training

1. Set the path of pretrained ResNeXt model in resnext/config.py
2. Set the path of datasets in tools/config.py
3. Run by train.sh

Testing

1. Set the path of five benchmark datasets in tools/config.py.
2. Put the trained model in ./ckpt/.
3. Run by test.sh

Settings of testing were set at the top of test.py, and you can conveniently change them as you need.

directory

-DM2F -dataset.py -train_1_baseline.py -data -O-HAZE -# O-HAZY NTIRE 2018 -GT -HAZY