In nearly total darkness, most objects fade from view, but AI has the ability to see. Scientists at MIT have developed a groundbreaking technique utilizing a deep neural network to identify objects in extremely low-light conditions. The team trained the network by providing it with 10,000 deliberately dark, grainy, and out-of-focus images alongside their intended transparent patterns. This approach allowed the neural network to learn what to anticipate and revealed hidden transparent objects by generating ripples in any available light.
To address the challenges of image blurring caused by camera defocus, the researchers incorporated principles of physics into the AI's training. The result is an innovative AI system capable of detecting and reconstructing hidden objects, making even the darkest environments appear illuminated.
While this technology has potential applications in nighttime photography, the MIT team is primarily focused on its implications for medicine. Traditionally, capturing minute details in biological materials requires high light levels, which can damage delicate tissues. This new AI technique can image these tissues at significantly lower and safer light levels. Additionally, it holds promise for astronomy by enabling the detection of extremely faint celestial objects without the need for bright illumination.