Research Interests
Image processing, computer vision and object detection, with an additional interest in Cyber & Physical Security.
Zhicheng Zou (Frederick)
PhD Candidate in Computer Science
Room 1.23, Visual Information Laboratory, 1 Cathedral Square
University of Bristol, BS1 5DD, United Kingdom
About
I am a PhD candidate in Computer Science at the University of Bristol, researching image processing, computer vision, and object detection in challenging real-world environments. My current work focuses on atmospheric turbulence mitigation for long-range imaging and video, with an emphasis on building systems that remain useful beyond the lab. My PhD is funded by the China Scholarship Council-University of Bristol PhD Scholarship, and my supervisors are Dr. Pui Anantrasirichai and Professor Alin Achim.
News / Updates
MMDL-Net preprint publicly available on SSRN
Our preprint, MMDL-Net: A Multimodal Deep Learning Network for Post-Collision Injury Prediction and Triage Support, is now publicly available on SSRN.
Conference abstract accepted to IRCOBI 2026
Our work on machine-learning-based post-collision injury prediction and emergency response support was accepted for presentation at the International Research Council on Biomechanics of Injury.
Started my PhD at the University of Bristol
I joined the Visual Information Laboratory to work on atmospheric turbulence mitigation, long-range imaging, and computer vision systems for challenging real-world environments.
Received the Achievement Award in the Computer Graphics Contest
My real-time GLSL ray tracing shader was recognised at Imperial College London for physically based rendering, soft shadows, global illumination, and BRDF-based material interactions.
Presented DeTurb at ACCV 2024
DeTurb explores atmospheric turbulence mitigation with deformable 3D convolutions and 3D Swin Transformers for clearer long-range video restoration.
Education
PhD in Computer Science
Researching video enhancement and inverse problems for challenging environments, with a focus on perceptual significance, atmospheric turbulence, and real-world deployment.
MSc in Security and Resilience
Built a broader systems perspective through cyber-physical security, infrastructure protection, behavioural science, web security, and computer graphics.
BSc in Computer Science
Developed a strong base in computer vision, distributed systems, software engineering, graphics, security, and applied machine learning through both coursework and collaborative projects.
Current Work
Real-Time Atmospheric Turbulence Mitigation and Object Recognition
Supervisors: Dr. Pui Anantrasirichai and Professor Alin Achim.
MMDL-Net: A Multimodal Deep Learning Network for Post-Collision Injury Prediction and Triage Support
Supervisors: Dr. Claire Baker and Professor Arnab Majumdar.
Collagenous Gastritis Case Tracking and Documentation
Tracking and documenting a Collagenous Gastritis case, with attention to clinical progression, evidence organisation, and longitudinal record-keeping. A detailed preprint is currently being prepared.
Awards
Achievement Award, Computer Graphics Contest 2025
Awarded for a real-time ray tracing shader in GLSL featuring physically based rendering, soft shadows, one-bounce global illumination, and BRDF-based material interactions. Score: 99.5 / 100.
People Choice Prize, CSS GAME-JAM
Won with the game “Toxic Tide” after two days of coding, collaboration, and rapid creative development around the themes of contamination and horror.
Bristol Plus Award
Recognised for extracurricular involvement and continued development in teamwork, communication, leadership, and problem-solving beyond degree requirements.
Publications
MMDL-Net: A Multimodal Deep Learning Network for Post-Collision Injury Prediction and Triage Support
A preprint exploring multimodal deep learning for post-collision injury prediction and triage support.
DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers
A framework for atmospheric turbulence mitigation in long-range imaging that combines geometric restoration with multi-scale transformer-based enhancement for sharper, clearer reconstructions.