The implementation of CVPR 2019 paper "Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements"
News (19/09/2019): Fix the broken link; our pretrained model and collected unaligned dataset are now available at OneDrive
- Our network can extract the background image layer devoid of reflection artifacts, as in the example:
- We captured a new dataset containing 450 unaligned image pairs that are considerably easier to collect. Image samples from our unaligned dataset are shown below:
- We introduce a simple but powerful alignment-invariant loss function to facilitate exploiting misaligned real-world training data. Finetuning on unaligned image pairs with our loss leads to sharp and reflection-free results, in contrast to the blurry ones when using a conventional pixel-wise loss (L1, L2, e.t.c.). The resulting images finetuned by different losses are shown below: (Left: Pixel-wise loss; Right: Ours)
- Python >=3.5, PyTorch >= 0.4.1
- Requirements: opencv-python, tensorboardX, visdom
- Platforms: Ubuntu 16.04, cuda-8.0
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7,643 cropped images with size 224 × 224 from Pascal VOC dataset (image ids are provided in VOC2012_224_train_png.txt, you should crop the center region with size 224 x 224 to reproduce our result).
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90 real-world training images from Berkeley real dataset
- 100 synthetic testing images from CEILNet dataset (testdata_reflection_synthetic_table2)
- 20 real testing images from Berkeley real dataset.
- Three sub-datasets, namely ‘Objects’, ‘Postcard’, ‘Wild’ from SIR^2 dataset
Once the data are downloaded, you must organize the dataset according to our code implementation (see the source code of datasets.CEILDataset, e.t.c.)
- Download our pretrained model from Google Drive and move
errnet_060_00463920.pt
tocheckpoints/errnet/
. - Evaluate the model performance by
python test_errnet.py --name errnet -r --icnn_path checkpoints/errnet/errnet_060_00463920.pt --hyper
- Reproduce our results by
python train_errnet.py --name errnet --hyper
- Check
options/errnet/train_options.py
to see more training options.
If you find our code helpful in your research or work please cite our paper.
@inproceedings{wei2019single,
title={Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements},
author={Wei, Kaixuan and Yang, Jiaolong and Fu, Ying and David, Wipf and Huang, Hua},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019},
}
If you find any problem, please feel free to contact me (kaixuan_wei at bit.edu.cn). A brief self-introduction is required, if you would like to get an in-depth help from me.
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Special thanks to @fqnchina and @ceciliavision for some discussions of this work.