/MG-Cycle-GAN

We convert the highlight removal problem to image-to-image translation by using cycle-consistent adversarial network (Cycle-GAN). The network can remove the specular highlight from natural images.

Primary LanguagePythonMIT LicenseMIT

Mask-Guided-Cycle-GAN-for-Specular-Highlight-Removal

We convert the highlight removal problem to image-to-image translation by using cycle-consistent adversarial network (Cycle-GAN). The network can remove the specular highlight from natural images.

We use a highlight mask estimated via the incorporation of the NMF method to guide the network.

Several examples of our result can be seen as follows.

Dependencies

  • python 3.7+
  • pytorch 1.1+ & tochvision (cu102+)
  • scikit-image

Dataset

  • SHIQ

    @InProceedings{fu-2021-multi-task,
author = {Fu, Gang and Zhang, Qing and Zhu, Lei and Li, Ping and Xiao, Chunxia},
title = {A Multi-Task Network for Joint Specular Highlight Detection and Removal},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2021},
pages = {7752-7761},
month = {June},
tags = {CVPR},
}

    You can download the data from Google Drive. The link is: https://drive.google.com/file/d/1RFiNpziz8X5qYPVJPl8Y3nRbfbWVoDCC/view?usp=sharing (~1G).

  • LIME

    @inproceedings{meka2018lime,
title={Lime: Live intrinsic material estimation},
author={Meka, Abhimitra and Maximov, Maxim and Zollhoefer, Michael and Chatterjee, Avishek and Seidel, 
Hans-Peter and Richardt, Christian and Theobalt, Christian},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={6315--6324},
year={2018}
}

    The download link is: https://download.mpi-inf.mpg.de/projects/LIME/LIME_TrainingData.rar

Network

train

  1. set the paths of the dataset in train_l.py, in line 44 opt.dataroot = 'SHIQ_data'. The SHIQ_data is the file path of the dataset.
  2. run train_l.py
  3. set the paths of the saved models achieved from step 2 (netG_A2B.pth,netG_B2A.pth), in line54 netG_A2B = Generator_H2F.from_file('model/netG_A2B.pth') and the dataset in train.py, which is similar to train_l.py.
  4. run train.py

test

  1. set the paths of the saved models achieved from step 4 (netG_A2B.pth) and the test dataset in test.py
  2. run test.py

Article

The article link is： https://www.sciencedirect.com/science/article/abs/pii/S0167865522002082

相关解答

(Due to already working, the project is rarely maintained and many questions cannot be answered in time, sorry for that.) The simple way to set up the data is to put the downloaded datasets directly in the network directory. The function to read the path can be seen in line 17 of the datasets.py file.

（由于已经工作，项目很少维护，很多问题无法及时解答，深感歉意。） 设置数据简单的方法是直接把下载来的数据集放在network目录下，读取路径的函数可以在 datasets.py 文件中第17行看到。