Images suffer from motion blur due to long exposure in poor light condition or rapid motion. Unlike conventional cameras, event-cameras do not suffer from motion blur. This is due to the fact that event-cameras provide events together with the exact time when they were triggered. In this project, we will make use of hybrid sensors which provide both conventional images and events such that we can exploit the advantages of both. By the end of this project you will have developed a great amount of experience in event-based vision, deep learning and computational photography.
Requirements:
- Background in computer vision and machine learning
- Deep learning experience preferable but not strictly required
- Programming experience in C++ and Python
Images suffer from motion blur due to long exposure in poor light condition or rapid motion. Unlike conventional cameras, event-cameras do not suffer from motion blur. This is due to the fact that event-cameras provide events together with the exact time when they were triggered. In this project, we will make use of hybrid sensors which provide both conventional images and events such that we can exploit the advantages of both. By the end of this project you will have developed a great amount of experience in event-based vision, deep learning and computational photography.
Requirements:
- Background in computer vision and machine learning - Deep learning experience preferable but not strictly required - Programming experience in C++ and Python
The goal is to develop an algorithm capable producing a blur-free image from the captured, blurry image, and events within the exposure time. To this end, synthetic data can be generated by our simulation framework which is able to generate both synthetic event data and motion blurred images. This data can be used by machine learning algorithms designed to solve the task at hand. At the end of the project, the algorithm will be adapted to perform optimally with real-world data.
The goal is to develop an algorithm capable producing a blur-free image from the captured, blurry image, and events within the exposure time. To this end, synthetic data can be generated by our simulation framework which is able to generate both synthetic event data and motion blurred images. This data can be used by machine learning algorithms designed to solve the task at hand. At the end of the project, the algorithm will be adapted to perform optimally with real-world data.
Mathias Gehrig (mgehrig at ifi.uzh.ch); Daniel Gehrig (dgehrig at ifi.uzh.ch)
Mathias Gehrig (mgehrig at ifi.uzh.ch); Daniel Gehrig (dgehrig at ifi.uzh.ch)