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ReflectNet: Separating Reflection and Transmission Images in the Wild

Patrick Wieschollek, Orazio Gallo, Jinwei Gu, Jan Kautz (ECCV 2018)

Abstract

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present ReflectNet, a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance that explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.

The following figure shows a common case and our estimation of the reflection and transmission images compared with state-of-the-art methods.

This repository contains the source code and the model for ReflectNet, our 2018 ECCV paper "Separating Reflection and Transmission Images in the Wild." To compare against existing state-of-the-art, we also implemented several previously published methods, which may be useful to others in the research community. Specifically, in addition to our ReflectNet, we offer implementations for:

• Polarization and statistical analysis of scenes containing a semireflector (Schechner et al. [1])
• A physically-based approach to reflection separation: From physical modeling to constrained optimization (Kong et al. [3])
• Single Image Reflection Suppression (Arvanitopoulos et al. [4])
• Separating reflections and lighting using independent components analysis (Farid and Adelson [5])

More Resources

• Project page
• Paper
• Dataset

Requirements

• OpenCV with Python bindings
• tensorpack 0.8.8 (pip install -U git+https://github.com/ppwwyyxx/tensorpack.git --user)
• dcraw
• TensorFlow >=1.3.0 (pip install tensorflow-gpu --user)

Performing Inference with ReflectNet

Download the data and run

user@host $ cd ReflectNet && ./run_example.sh

Citation

If you use the code in this repository or the dataset, please cite our paper:

@inproceedings{eccv2018/Wieschollek,
  author    = {Patrick Wieschollek and
               Orazio Gallo and
               Jinwei Gu and
               Jan Kautz
               },
  title     = {Separating Reflection and Transmission Images in the Wild},
  booktitle = {European Conference on Computer Vision (ECCV)},
  month     = {September},
  year      = {2018}
}

References

[1] Schechner, Y.Y., Shamir, J., Kiryati, N., "Polarization and statistical analysis of scenes containing a semireflector," Journal of the Optical Society of America, 2000.<br/> [2] Kong, N., Tai, Y.W., Shin, J.S., "A physically-based approach to reflection separation: From physical modeling to constrained optimization," IEEE TPAMI, 2014.<br/> [3] Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D., "A generic deep architecture for single image reflection removal and image smoothing," IEEE ICCV, 2017.<br/> [4] Arvanitopoulos Darginis, N., Achanta, R., Süsstrunk, S., "Single image reflection suppression," IEEE CVPR, 2017.<br/> [5] Farid, H., Adelson, E.H., "Separating reflections and lighting using independent components analysis," IEEE CVPR, 1999.<br/>