Engineer to combine math, machine learning and signal processing to lay groundwork for high-resolution microscope

Like our eyes, microscopes are limited in what they can see because of their resolution, or their ability to see detail. The detail, or information, from the object is there, but some of it gets lost as the light reflecting off of the object moves through the air.

“The whole premise of this is built on one single fact—the way light interacts with any matter is linear,” said Kamilov, assistant professor of electrical & systems engineering and computer science & engineering. “But the reality is that the interaction is actually not linear.”

For example, if you shine a flashlight through your hand, you can’t see the source of the light because it’s bending, and that is nonlinearity. With a single cell, the bending is so light that it is nearly transparent, which is linear.

When light interacts with a cell or an object, the light going out of the cell loses the information it gathers from that interaction. But because of that interaction, there are fluctuations in the vicinity of that cell that work with such matter and get retransformed and remitted. Those fluctuations are encoded into the nonlinearity of the interaction, but today’s microscopes are unable see this, Kamilov said.

“We want to take into account this nonlinear interaction of light, objects and premises, and if we do it correctly, we can extract that information, which normally disappears in a current microscope and is treated as ‘noise,'” Kamilov said. “We want to decode the information from the noise and add it back into the resolution, and that should give us features that are smaller than the resolution limit.”

Kamilov said there are two types of noise: imperfections and mathematical noise that is the result of science’s current limitations. It is the mathematical noise that he wants to capture.


Image Credit:  Washington University in St. Louis 

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