RCDNet

A PyTorch implementation of RCDNet based on CVPR 2020 paper A Model-driven Deep Neural Network for Single Image Rain Removal.

Requirements

• Anaconda

• PyTorch

conda install pytorch=1.10.1 torchvision cudatoolkit -c pytorch

Dataset

Rain100L, Rain100H, Rain1400 and SPA-Data are used, download these datasets and make sure the directory like this:

|-- data     
    |-- rain100L
        |-- train
            |-- rain
                norain-1.png
                ...
            `-- norain
                norain-1.png
                ...
        `-- test                                                        
    |-- rain100H
        same as rain100L
    `-- rain1400
        same as rain100L                       
    |-- spa
        same as rain100L

Usage

You can easily train and test the model by running the script below. If you want to try other options, please refer to utils.py.

Train Model

python main.py --data_name rain100L --seed 0

Test Model

python main.py --data_name rain100H --model_file result/rain100H.pth

Benchmarks

The models are trained on one NVIDIA GeForce RTX 3090 GPU (24G). All the hyper-parameters are the default values.

Method	Rain100L		Rain100H		Rain1400		SPA-Data		Download
	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
	Ours	40.29	0.9869	32.02	0.9180	33.28	0.9494	37.81		0.9631	MEGA
Official	40.00	0.9860	31.28	0.9093	33.04	0.9472	41.47	0.9834	Github

Results

More results could be downloaded from Dropbox.