Science Daily December 12, 2018
Using deep neural network technique researchers at MIT reconstructed transparent objects from images of those objects, taken in almost pitch-black conditions. A computer was trained to recognize more than 10,000 transparent glass-like etchings, based on extremely grainy images of those patterns, with about one photon per pixel. They found that the computer learned to reconstruct the transparent object from the new grainy image, not included in the training data. The technique is of practical importance for medical imaging to lower the exposure of the patient to harmful radiation, and for astronomical imaging…read more. TECHNICAL ARTICLE
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). Credit: Courtesy of the researchers