（1-复现使用手册）Restormer: Efficient Transformer for High-Resolution Image Restoration CVPR2022

目录

一、Installation

1.配置基本环境

2.下载数据集

2.1命令行方式

2.1.1安装go指令

2.1.2 配置文件修改变量

2.1.3 下载数据集

2.2 网站直接下载（推荐）

二、Demo

三、Training and Evaluation

四、Results

Image Deraining ​

Single-Image Motion Deblurring

Defocus Deblurring

Gaussian Image Denoising

Real Image Denoising​

五、Related Works

paper:[2111.09881] Restormer: Efficient Transformer for High-Resolution Image Restoration (arxiv.org)

Git：GitHub - swz30/Restormer: [CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

[（2-论文精读、代码分析）Restormer: Efficient Transformer for High-Resolution Image Restoration CVPR2022( "（2-论文精读、代码分析）Restormer: Efficient Transformer for High-Resolution Image Restoration CVPR2022")

一、Installation

This repository is built in PyTorch 1.8.1 and tested on Ubuntu 16.04 environment (Python3.7, CUDA10.2, cuDNN7.6). Follow these intructions

1.配置基本环境

 git clone https://github.com/swz30/Restormer.git

    
 cd Restormer
    
    
    
    

 conda create -n pytorch181 python=3.7

    
 conda activate pytorch181
    
    
    
    

 conda install pytorch=1.8 torchvision cudatoolkit=10.2 -c pytorch

    
 pip install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm
    
 pip install einops gdown addict future lmdb numpy pyyaml requests scipy tb-nightly yapf lpips
    
    
    
    

    python setup.py develop --no_cuda_ext
    
    

2.下载数据集

2.1命令行方式

To be able to download datasets automatically you would need go and gdrive installed.

2.1.1安装go指令

 curl -O https://storage.googleapis.com/golang/go1.11.1.linux-amd64.tar.gz

    
 mkdir -p ~/installed
    
 tar -C ~/installed -xzf go1.11.1.linux-amd64.tar.gz
    
 mkdir -p ~/go
    
    
    
    

第一个指令：go1.11.1.linux-amd64.tar.gz 文件会被下载到当前工作目录下

后面三个指令就是解压并安装 Go

记得修改对应的路径哈~，比如我的就会这么改，不然就会安到默认路径（比如我的是home/ ）

mkdir -p /data1/zhangjiening/Restormer/installed

tar -C /data1/zhangjiening/Restormer/installed -xzf go1.11.1.linux-amd64.tar.gz

mkdir -p /data1/zhangjiening/Restormer/go

2.1.2 配置文件修改变量

vim ~/.bashrc 打开配置文件，在最后添加这两行，Go 的可执行文件目录添加到系统 PATH 变量中了

 export GOPATH=$HOME/go

    
 export PATH=$PATH:$HOME/go/bin:$HOME/installed/go/bin
    
    
    
    

还是修改路径的问题：

export GOPATH=/data1/zhangjiening/Restormer/go
export PATH=$PATH:/data1/zhangjiening/Restormer/go/bin:/data1/zhangjiening/Restormer/installed/go/bin

2.1.3 下载数据集

    go get github.com/prasmussen/gdrive
    
    

2.2 网站直接下载（推荐）

GitHub - prasmussen/gdrive: Google Drive CLI Client

不想上面这么麻烦的话，直接下载呗

直接把文件扔到 ~/go/bin 路径下

二、Demo

python demo.py --task Task_Name --input_dir path_to_images --result_dir save_images_here

 python demo.py --task Deraining --input_dir path_to_images --result_dir save_images_here

    
 python demo.py --task Motion Deblurring --input_dir path_to_images --result_dir save_images_here
    
 python demo.py --task Defocus Deblurring --input_dir path_to_images --result_dir save_images_here
    
 python demo.py --task Denoising --input_dir path_to_images --result_dir save_images_here
    
    
    
    

Example usage to perform Defocus Deblurring on a directory of images:

    python demo.py --task Single_Image_Defocus_Deblurring --input_dir './demo/degraded/' --result_dir './demo/restored/'
    
    

Example usage to perform Defocus Deblurring on an image directly:

    python demo.py --task Single_Image_Defocus_Deblurring --input_dir './demo/degraded/portrait.jpg' --result_dir './demo/restored/'
    
    

三、Training and Evaluation

四、Results

SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

Image Deraining

Single-Image Motion Deblurring

Defocus Deblurring

Gaussian Image Denoising

Grayscale	Color

Real Image Denoising

五、Related Works

• Learning Enriched Features for Fast Image Restoration and Enhancement, TPAMI 2022. Paper | Code
• Multi-Stage Progressive Image Restoration, CVPR 2021. Paper | Code
• Learning Enriched Features for Real Image Restoration and Enhancement, ECCV 2020. Paper | Code
• CycleISP: Real Image Restoration via Improved Data Synthesis, CVPR 2020. Paper | Code

后续会更新Restormer精读笔记和复现笔记~相关link随时同步在这里