From an original transparent etching (far right), engineers produced a photograph in the dark (top left), then attempted to reconstruct the object using first a physics-based algorithm (top right), then a trained neural network (bottom left), before combining both the neural network with the physics-based algorithm to produce the clearest, most accurate reproduction (bottom right) of the original object. Courtesy of the researchers
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.
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