Awesome
Awesome-Image-Blending 
A curated list of papers, code and resources pertaining to image blending. Image blending aims to blend the foreground into the background seamlessly by coping with the unnatural boundary between foreground and background. For more complete resources on general image composition, please refer to Awesome-Image-Composition.
<p align='center'> <img src='./figures/task.jpg' width=80% /> </p>Contributing
Contributions are welcome. If you wish to contribute, feel free to send a pull request. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a pull request.
Survey
A brief review on image blending is included in the following survey on image composition:
Li Niu, Wenyan Cong, Liu Liu, Yan Hong, Bo Zhang, Jing Liang, Liqing Zhang: "Making Images Real Again: A Comprehensive Survey on Deep Image Composition." arXiv preprint arXiv:2106.14490 (2021). [arXiv] [slides]
Papers
Optimization-based Methods
- Lingzhi Zhang, Tarmily Wen, Jianbo Shi: "Deep Image Blending." WACV (2020) [pdf] [arXiv] [code]
- Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang: "GP-GAN: Towards Realistic High-Resolution Image Blending." ACM MM (2019) [arXiv] [code]
Feed-forward Methods
- Shuo Wang, Weijie Lv, Xinyuan Zhao, Xinyu Zhang, Junyu Su, Long Zeng: "Refined-mask guided multi-stream blending network." Multimedia Tools and Applications (2023) [pdf]
- Yazhou Xing, Yu Li, Xintao Wang, Ye Zhu, Qifeng Chen: "Composite photograph harmonization with complete background cues." ACM MM (2022) [pdf]
- He Zhang, Jianming Zhang, Federico Perazzi, Zhe Lin, Vishal M. Patel: "Deep Image Compositing." WACV (2021) [pdf]