Turbulence-free computational ghost imaging

Turbulence-free images cannot be produced by conventional computational ghost imaging because calculated light is not affected by the same atmospheric turbulence as real light. In this article, we first addressed this issue by measuring the photon number fluctuation autocorrelation of the signals generated by a conventional computational ghost imaging device. Our results illustrate how conventional computational ghost imaging without structural changes can be used to produce turbulence-free images.

Can a conventional optical camera realize turbulence-free imaging?

Atmospheric turbulence is a serious problem for traditional optical imaging, especially for satellite and aircraft-to-ground imaging. Here, we report a novel and practical phenomenon in which turbulence-free images can be reconstructed on a conventional optical camera based on the accumulation of sunlight intensity and photon number fluctuation autocorrelation. Different from conventional ghost imaging, this method can obtain turbulence-free images, and its imaging speed is comparable to that of traditional optical imaging. Moreover, by adding photon number fluctuation autocorrelation algorithm software, almost all optical cameras, including mobile phone cameras, can realize this function without changing the structure of the original camera.

True color night vision correlated imaging based on intensity correlation of light

Night vision imaging is a technology that converts non-visible object to human eyes into visible image in night and other low light environments. However, the conventional night vision imaging can only directly produce grayscale image. Here, we propose a novel night vision imaging method based on intensity correlation of light. The object's information detected by infrared non-visible light is expressed by visible light via the spatial intensity correlation of light. With simple data processing, a color night vision image can be directly produced by this approach without any pseudo-color image processing. Theoretical and experimental results show that a color night vision image comparable to classical visible light imaging quality can be obtained by this method. Surprisingly, the color colorfulness index of the reconstructed night vision image is significantly better than that of the conventional visible light image and pseudo-color night vision image. Although the reconstructed image can not completely restore the natural color of the object, the color image obtained by this method is more natural sense than that obtained by other pseudo-color image processing methods.