Single-Image Reflection Removal via a Two-Stage Background Recovery Process
Codes for the paper "Single-Image Reflection Removal via a Two-Stage Background Recovery Process"
Codes are implemented on pytorch>=0.2.1
How to use
Training:
- Training process uses images from VOC2012 for reflection image simulation.
- The network for the first stage process:
python train_1st_stage.py --imgs_dir your_voc12_path/VOC2012/JPEGImages/
- The network for the 2nd stage process:
python train_2nd_stage.py --imgs_dir your_voc12_path/VOC2012/JPEGImages/
Trained models will be saved in the folder ./model_para
Testing:
- A single image demo:
python one_img_demo.py --img_dir one_image_path(e.g. imgs/1.png) --net_ini_pkl path_to_trained_models/Net_1st_stage.pkl --netG_img_pkl path_to_trained_models/Net_2nd_stage.pkl
- Evaluate on SIR2 benchmark:
python benchmark_imgs_process.py --bechmark_dir SIR2_dataset_path --net_ini_pkl path_to_trained_models/Net_1st_stage.pkl --netG_img_pkl path_to_trained_models/Net_2nd_stage.pkl
- Pre-trained models are provided
- (K-means threshold coeficents 0.5 and 0.5 can show better perceptual performance. But 0.2 and 0.8 can give better background fidelity)
Citation
@Article{li2019rm2s,
author = {Li, Tingtian and Lun, Daniel P.K.},
title = {Single-Image Reflection Removal via a Two-Stage Background Recovery Process},
journal = {IEEE Signal Processing Letters},
year = {2019},
}