Staff profile

Shuang Chen is a diligent and ambitious PhD student, pursuing his studies at Durham University with a strong focus on deep learning, computer vision, image processing, and image inpainting. Prior to this, Shuang Chen achieved a master's degree in Computer Vision, Machine Learning and Robltics from the University of Surrey and a bachelor's degree in automation from Shandong University of Technology.

• Computer Vision, Image Processing, Image Restoration, Image Inpainting.

Conference Paper

• Depth-Aware Endoscopic Video Inpainting
  
  Xiatian Zhang, F., Chen, S., Xie, X., & Shum, H. P. (in press). Depth-Aware Endoscopic Video Inpainting. Presented at 27th International Conference on Medical Image Computing and Computer Assisted Intervention, Marrakesh, Morocco.
• Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots
  
  Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (in press). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA.
• SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM
  
  Chen, S., Zhang, H., Atapour-Abarghouei, A., & Shum, H. P. H. (2025). SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM. In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 461-471). IEEE. https://doi.org/10.1109/WACV61041.2025.00055
• MxT: Mamba x Transformer for Image Inpainting
  
  Chen, S., Atapour-Abarghouei, A., Zhang, H., & Shum, H. P. H. (2024). MxT: Mamba x Transformer for Image Inpainting. In Proceedings of the 2024 British Machine Vision Conference. British Machine Vision Association.
• A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
  
  Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926917

Journal Article

• Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey
  
  Vijendran, M., Deng, J., Chen, S., Ho, E. S. L., & Shum, H. P. H. (2025). Artificial intelligence for geometry-based feature extraction, analysis and synthesis in artistic images: a survey. Artificial Intelligence Review, 58(2), Article 64. https://doi.org/10.1007/s10462-024-11051-3
• One-Index Vector Quantization Based Adversarial Attack on Image Classification
  
  Fan, H., Qin, X., Chen, S., Shum, H. P. H., & Li, M. (2024). One-Index Vector Quantization Based Adversarial Attack on Image Classification. Pattern Recognition Letters, 186, 47-56. https://doi.org/10.1016/j.patrec.2024.09.001
• HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention
  
  Chen, S., Atapour-Abarghouei, A., & Shum, H. P. H. (2024). HINT: High-quality INpainting Transformer with Mask-Aware Encoding and Enhanced Attention. IEEE Transactions on Multimedia, 26, 7649-7660. https://doi.org/10.1109/TMM.2024.3369897
• INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
  
  Chen, S., Atapour-Abarghouei, A., Ho, E. S., & Shum, H. P. (2023). INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network. Software Impacts, 17, Article 100517. https://doi.org/10.1016/j.simpa.2023.100517