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Night vision is the ability to see in low-light conditions. However, conventional night vision imaging technology is limited by the requisite high-performance infrared focal plane array. In this article, we propose a novel scheme of color night vision imaging without the use of an infrared focal plane array. In the experimental device, the two-wavelength infrared laser beam reflected by the target is modulated by a spatial light modulator, and the output light is detected by a photomultiplier tube. Two infrared night vision images are reconstructed by measuring the second-order intensity correlation function between two light fields. Thus, the processing mode of optical electric detection in conventional night vision imaging is transformed into the processing mode of light field control. Furthermore, two gray images with different spectra are processed to form a color night vision image. We show that a high-quality color night vision image can be obtained by this method.

In this article, the color of ghost imaging (GI) was studied theoretically and experimentally. The theoretical analysis and experimental data show that the color of GI with rotating ground glass plate and computational GI are the same as the light source. If multiwavelength source is used in these schemes, a full color image without distortion can be obtained. In contrast, the color of GI with spatial light modulator as well as that in a quantum system is a superimposed one, depending on the idle and object light beams, and following the principle of light color superposition. Correspondingly, a full color image can also be obtained under the condition of multiwavelength source, but with color distortion existing.

There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging. In this article, we report a turbulence-immune CGI method based on a multi-scale generative adversarial network (MsGAN). Here, the conventional CGI framework is not changed, but the conventional CGI coincidence measurement algorithm is optimized by an MsGAN. Thus, the satisfactory ghost image can be reconstructed by training the network, and the visual effect can be significantly improved.