DynamicZoom is a cutting-edge video enhancement system developed as part of the Computer Vision (CS/ECE 766) course at UW-Madison. The project addresses the common problem of video quality degradation during digital zoom by implementing real-time super-resolution technologies.
Key Features
- Real-time video resolution enhancement
- Region of Interest (ROI) based processing
- GPU-accelerated processing using PyTorch and CUDA
- Support for multiple upscaling models
- Minimal processing latency (7-8ms for 720p to 4K)
Performance Highlights
- Achieves high-quality upscaling with inference speeds of 7-8ms
- Comparable quality to SwiftSRGAN with significantly faster processing
- Implements optimized Bicubic++ architecture for real-time performance
- Optimized for real-time applications in sports broadcasting, surveillance, and AR/VR
Applications
- Sports Analysis: Enhanced slow-motion replays
- Augmented Reality: Improved visual clarity
- Security Systems: Better surveillance footage
- Search and Rescue: Enhanced detail in critical operations
Technologies Used
- Python
- PyTorch
- CUDA
- Bicubic++ Model
- Advanced Parallel Processing
Team:
- Shantanu Vichare
- Dario Placencio
- Nico Ranabhat
- Hemanth Sridhar Nakshatri