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Single-Image-Reflection-Removal-Beyond-Linearity

Paper

Single Image Reflection Removal Beyond Linearity.

Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, and Shengfeng He*

Requirement

Reflection Synthesis

cd ./Synthesis
python3 ./train.py --dataroot path_to_dir_for_reflection_synthesis/ --gpu_ids 0 --save_epoch_freq 1 --batchSize 10

or you can directly:

bash ./synthesis_train.sh
python3 ./test.py --dataroot path_to_dir_for_synthesis/ --gpu_ids 0 --which_epoch 130 --how_many 1

or you can directly:

bash ./synthesis_test.sh

Here is the pre-trained model. And to generate the three types of reflection images, you can use these original images which are from perceptual-reflection-removal.

Due to the copyright, the real reflection images are not released here.

Reflection Removal

cd ./Removal
python3 ./train.py --dataroot path_to_dir_for_reflection_removal/ --gpu_ids 0 --save_epoch_freq 1 --batchSize 5 --which_type focused

or you can directly:

bash ./removal_train.sh
python3 ./test.py --dataroot path_to_dir_for_reflection_removal/ --which_type focused --which_epoch 130 --how_many 1

or you can directly:

bash ./removal_test.sh

Here are the pre-trained models which are trained on the three types of synthetic dataset.

Here are the synthetic training set and testing set for reflection removal.

To evaluate on other datasets, please finetune the pre-trained models or re-train a new model on the specific training set.

Acknowledgments

Part of the code is based upon pytorch-CycleGAN-and-pix2pix.

Citation

@InProceedings{Wen_2019_CVPR,
  author = {Wen, Qiang and Tan, Yinjie and Qin, Jing and Liu, Wenxi and Han, Guoqiang and He, Shengfeng},
  title = {Single Image Reflection Removal Beyond Linearity},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}