bdn-refremv

Deep Bidirectional Estimation for Single Image Reflection Removal. This package is the implementation of the paper:

Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal
Jie Yang*, Dong Gong*, Lingqiao Liu, Qinfeng Shi.
In European Conference on Computer Vision (ECCV), 2018. (* Equal contribution)

Requirements

Python packages
```
pytorch>=0.4.0
numpy
pillow
```
An NVIDIA GPU and CUDA 9.0 or higher

Usage

Download our pretrained model here. Unpack the archive into model folder.
Put test images into samples folder, and run script bash test.sh.

Examples and Real-world Testing Images

Two examples (on real-world images taken by a mobile phone) are shown in the following: from left to right: I (observed image with reflection), B (recovered reflection-free image) and R (the intermediate reflection image). Please see details and examples in our paper.

More real-world reflection images can be found in /samples for testing.

Datasets

The synthetic datasets used for training and testing in our paper:

Citation

If you use this code for your research, please cite our paper:

@inproceedings{eccv18refrmv,
  title={Seeing deeply and bidirectionally: a deep learning approach for single image reflection removal},
  author={Yang, Jie and Gong, Dong and Liu, Lingqiao and Shi, Qinfeng},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={654--669},
  year={2018}
}