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Deep-Learning Technique Reveals “Invisible” Objects in the Dark

 

Originally published in MIT News, December 12, 2018.

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Method could illuminate features of biological tissues in low-exposure images.

Small imperfections in a wine glass or tiny creases in a contact lens can be tricky to make out, even in good light. In almost total darkness, images of such transparent features or objects are nearly impossible to decipher. But now, engineers at MIT have developed a technique that can reveal these “invisible” objects, in the dark.

In a study published today in Physical Review Letters, the researchers reconstructed transparent objects from images of those objects, taken in almost pitch-black conditions. They did this using a “deep neural network,” a machine-learning technique that involves training a computer to associate certain inputs with specific outputs — in this case, dark, grainy images of transparent objects and the objects themselves.

The team trained a computer to recognize more than 10,000 transparent glass-like etchings, based on extremely grainy images of those patterns. The images were taken in very low lighting conditions, with about one photon per pixel — far less light than a camera would register in a dark, sealed room. They then showed the computer a new grainy image, not included in the training data, and found that it learned to reconstruct the transparent object that the darkness had obscured.

The results demonstrate that deep neural networks may be used to illuminate transparent features such as biological tissues and cells, in images taken with very little light.

“In the lab, if you blast biological cells with light, you burn them, and there is nothing left to image,” says George Barbastathis, professor of mechanical engineering at MIT. “When it comes to X-ray imaging, if you expose a patient to X-rays, you increase the danger they may get cancer. What we’re doing here is, you can get the same image quality, but with a lower exposure to the patient. And in biology, you can reduce the damage to biological specimens when you want to sample them.”

Barbastathis’ co-authors on the paper are lead author Alexandre Goy, Kwabena Arthur, and Shuai Li.

Deep Dark Learning

Neural networks are computational schemes that are designed to loosely emulate the way the brain’s neurons work together to process complex data inputs. A neural network works by performing successive “layers” of mathematical manipulations. Each computational layer calculates the probability for a given output, based on an initial input. For instance, given an image of a dog, a neural network may identify features reminiscent first of an animal, then more specifically a dog, and ultimately, a beagle. A “deep” neural network encompasses many, much more detailed layers of computation between input and output.

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