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rainremoval's Introduction

Single Image Deraining via Scale-space Invariant Attention Neural Network

The paper[Link] is accepted by ACMMM2020(oral). This is pytorch implementation for our paper. The code is based on the Pix2Pix.

Prerequisites

  • python 3.6
  • pytorch 1.1.0
  • opencv-python
  • visdom

Pretrained Models

You could directly find our pretrained models in the directory.

Usage

To train the model, please run

python train.py --dataroot [your dataset path] --name rpdnet_lstm_100l --model rpdnet --gpu_ids 3 
--batch_size 18 --output_nc 3 --input_nc 3 --niter 100 --niter_decay 0 --save_epoch_freq 10
--display_freq 40 --dataset_mode [rain100l|rain100h|rain1400] --display_freq 20 --lr 2e-4
--color_space 'rgb' --no_html --display_env lstm

To generate images, please run

python eval.py --dataset_path [your dataset path] --model_path [your model path] 
--save_path [your save path] --gpu_id 0

Performance

The evaluation metrics are provided by Ren. The performances on the four datasets are listed below:

Dataset PSNR SSIM
Rain100L 38.80 0.984
Rain100H 30.33 0.909
Rain1400 32.80 0.946
Rain12 37.42 0.967

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