Project overview
This project explores advanced techniques for Image Dehazing, focusing on two key directions: reducing computational costs through joint super-resolution training and enhancing accuracy using depth information from stereo and LiDAR systems.
Efficient dehazing via super-resolution
Awarded the Best Paper at IEEE IS3C 2023, this work investigates the synergy between dehazing and Super-Resolution (SR). By employing a Joint Training strategy, we reduce the computational overhead typically required for high-quality restoration, making it suitable for real-time ADAS applications.
Depth-aware dehazing with LiDAR
Presented at IEEE ICASI 2023, this research leverages Stereo Depth Estimation Networks and LiDAR-Assisted Cameras. The fusion of precise depth data helps the model better estimate haze density (transmission map), leading to cleaner more physically accurate dehazing results.
Related publications
Reducing Computational Requirements of Image Dehazing Using Super-Resolution Networks
IEEE IS3C 2023, Taichung, Taiwan · 2023
Using Stereo Depth Estimation Network and LiDAR-Assisted Camera for Dehazing
2023 9th IEEE International Conference on Applied System Innovation (ICASI2023), Chiba, Japan · 2023