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Visible Watermark Removal via Self-calibrated Localization and Background Refinement


Introduction

This is the official code of the following paper:

Visible Watermark Removal via Self-calibrated Localization and Background Refinement[1] <br>Jing Liang<sup>1</sup>, Li Niu<sup>1</sup>, Fengjun Guo<sup>2</sup>, Teng Long<sup>2</sup> and Liqing Zhang<sup>1</sup> <br><sup>1</sup>MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University <br><sup>2</sup>INTSIG<br> (ACM MM 2021 | Bibtex)

SLBR Network

Here is our proposed SLBR(Self-calibrated Localization and Background Refinement). Top row depicts the whole framework of SLBR and bottom row elaborates the details of our proposed three modules.

<div align="center"> <img src="figs/framework.png" width = "100%" height = "100%" alt="Some examples of inharmonious region" align=center /> </div> <div align="center"> <img src="figs/blocks.jpg" width = "100%" height = "100%" alt="Some examples of inharmonious region" align=center /> </div>

Quick Start

Install

Data Preparation

In this paper, we conduct all of the experiments on the latest released dataset CLWD[2] and LVW[3]. You can contact the authors of LVW to obtain the dataset.

Train and Test

Pretrained Model

Here is the model trained on CLWD dataset:

Visualization Results

We also show some qualitative comparision with state-of-art methods:

<div align="center"> <img src="figs/bg_comparison.png" width = "90%" height = "90%" alt="Some examples of inharmonious region" align=center /> </div>

Acknowledgements

Part of the code is based upon the previous work SplitNet[4].

Citation

If you find this work or code is helpful in your research, please cite:

@inproceedings{liang2021visible,
  title={Visible Watermark Removal via Self-calibrated Localization and Background Refinement},
  author={Liang, Jing and Niu, Li and Guo, Fengjun and Long, Teng and Zhang, Liqing},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={4426--4434},
  year={2021}
}

Resources

We have summarized the existing papers, codes, and datasets on visible watermark removal in the following repository: https://github.com/bcmi/Awesome-Visible-Watermark-Removal

Reference

[1] Jing Liang, Li Niu, Fengjun Guo, Teng Long and Liqing Zhang. 2021. Visible Watermark Removal via Self-calibrated Localization and Background Refinement. In Proceedings of the 29th ACM International Conference on Multimedia. download

[2] Liu, Yang and Zhu, Zhen and Bai, Xiang. 2021. WDNet: Watermark-Decomposition Network for Visible Watermark Removal. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.

[3] Danni Cheng, Xiang Li, Wei-Hong Li, Chan Lu, Fake Li, Hua Zhao, and WeiShi Zheng. 2018. Large-scale visible watermark detection and removal with deep convolutional networks. In Chinese Conference on Pattern Recognition and Computer Vision. 27–40.

[4] Xiaodong Cun and Chi-Man Pun. 2020. Split then Refine: Stacked Attentionguided ResUNets for Blind Single Image Visible Watermark Removal. arXiv preprint arXiv:2012.07007 (2020).