At first sight, this research has counterintuitive results: Using the ‘noise’ (like ‘snow’ in a television picture, or random bad pixels in a picture) to make an image better. Normally you want this kind of noise removed from an image, but two researchers at Princeton University (New Jersey, USA) have developed a way to use ‘noise’ to augment what would otherwise be an unintelligible image.
The key experiment by Jason Fleischer, assistant professor of electrical engineering and Dmitri Dylov, electrical engineering graduate student, started with an image created by a laser beam passing through glass engraved with a grid and numbering system. The beam then passed through a plastic sheet with a translucency similar to cellophane tape. This broke the image into many fuzzy unreadable elements similar to looking at a scene through fog. Finally, in front of the receiver used to capture the light, the experimenters placed a crystal of strontium barium niobate (SBN). This material has many unusual properties, the most important in this case is a response of the crystal to an electrical field, which can be used to change the degree of ‘phase conjugation’ (a ferroelectric property of SBN to combine light). In a sense, the SBN crystal can be ‘tuned’ to add the light diffracted by the semi-transparent plastic back into the laser beam. The result is a much clearer image.
“We used noise to feed signals,” Dylov said. “It’s as if you took a picture of a person in the dark, and we made the person brighter and the background darker so you could see them. The contrast makes the person stand out.”
The technique, known as “stochastic resonance,” only works for the right amount of noise, as too much can overwhelm the signal. It has been observed in a variety of fields, ranging from neuroscience to energy harvesting, but never has been used this way for imaging.
Based on the results of their experiment, Fleischer and Dylov developed a new theory for how noisy signals move through nonlinear materials, which combines ideas from the fields of statistical physics, information theory and optics.
Their theory provides a general foundation for nonlinear communication that can be applied to a wide range of technologies. The researchers plan to incorporate other signal processing techniques to further improve the clarity of the images they generate and to apply the concepts they developed to biomedical imaging devices, including those that use sound and ultrasound instead of light.
[Source: Princeton University]
The physics and materials science behind this technique are surprisingly general in potential application (despite a very high level of complexity). As the researchers say, one day this approach may help pilots see in the fog…and sort out many other murky situations.