$360K Grant Aims to Advance Long-Range Imaging With Machine Learning

Aside from causing bumpy air travel, atmospheric turbulence can also affect aerial imaging systems used for surveillance, astronomy and more. Long-range imaging can be especially difficult, with pictures often ending up distorted and of little use because of the chaotic flow of air between the camera and object being photographed.

A University of Maryland expert in machine learning and computational imaging has received a $360,000 grant from the Army Research Office (ARO) to address this challenge. Christopher Metzler, an assistant professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS), was awarded the funding through the ARO’s Early Career Program.

The award will support a three-year project to develop a system that uses high-speed cameras and machine learning algorithms to instantaneously create clearer images, even in areas of extreme turbulence.

“Regardless of how good your optics are, (and) no matter how much you spend, you’re fundamentally limited by the atmosphere,” Metzler said.

The current approach toward long-range imaging focuses on measuring the level of distortion, then gradually adjusting the optical system to compensate for the atmospheric interference, he said.

But these types of systems are very difficult to use when trying to capture an object moving at high speeds. To overcome this, Metzler—working with his graduate students and others—is using neuromorphic cameras, also known as event cameras, which are triggered by movement and overall changes in intensity to capture data under a variety of conditions.

Traditional cameras used for long-range imaging can capture visuals at a rate of a hundred frames per second. The neuromorphic cameras can capture 10,000 frames per second, while using relatively little power.

Metzler plans to design novel machine learning algorithms to process this avalanche of nontraditional imaging data, as well as unique optical hardware that can capture more diverse measurements filled with more information.

One team member, doctoral student Sachin Shah, is the lead author on a paper recently presented at the IEEE/CVF Computer Vision and Pattern Recognition Conference held in Seattle that describes some of the strategies the researchers expect to employ.