Different from commercial digital cameras, an ultrafast optical imaging system based on photonic time stretch technique can take millions pictures per second. A research team led by Dr Chao Wang, a Lecturer in the School of Engineering and Digital Arts, has been working on this completely new type of imaging technology under the support of a Marie Curie Career Integration Grant. New ultrafast imaging technique has enabled new discoveries in science, engineering and medicine. For instance, the ultrafast nature makes it an ideal tool for high-throughput screening of rare circulating cancer cells in human blood towards early diagnosis of cancers.
However, the imaging instruments continuously produce a deluge of image data that can overwhelm even the most advanced back-end electronic circuits and digital image processors, and cause huge energy consumption. To tackle the Big Data challenges in ultrafast imaging, recently, Dr Wang’s team has developed a novel photonic compressive sensing technique that can instantaneously process and compress the massive image data in the optical domain without the need for sophisticated electronic circuits and digital processors. Full image can be reconstructed from much less captured data without missing useful information. This technique has been successively applied in data-compressed ultrafast optical coherence tomography. This work is a collaborative effort between Dr Wang’s team and Dr Stuart Gibson’s team from the School of Physical Sciences (SPS), bringing together complementary expertise of ultrafast imaging hardware and reconstruction algorithms from two teams.
Preliminary results of this work were presented at the 2016 IEEE Photonics Conference and selected as one of the top papers at the conference. A full-paper was published in IEEE Photonics Journal as an INVITED PAPER. The paper can be viewed here https://doi.org/10.1109/JPHOT.2017.2716179
The lead author of this research is EDA PhD student, Mr Chaitanya K Mididoddi. Other authors include EDA PhD student, Mr Guoqing Wang, SPS PhD student, Mr Fangliang Bai, and Dr Stuart Gibson, a Senior Lecturer in SPS.
This research was supported by the Royal Society and Innovate UK.