Recovery of encoded information from conjugate superimposed perfect vortex beams in scattering environments.

Using orbital angular momentum (OAM) encoding for signal transmission enables optical communication in the spatial domain. However, during the transmission process of vortex optical communication, environmental factors such as atmospheric turbulence and haze cause scattering effects, resulting in the degradation of signal quality and increasing the complexity of decoding. Our goal is to design a framework that can recover the encoded signal from the speckle field, reducing the effects of scattering. We have designed a neural network model that combines a generative adversarial network (GAN) and U-Net, which utilizes the image segmentation capability of U-Net to guide the GAN in generating accurate information. We have demonstrated its effectiveness in experiments, with Pearson correlation coefficient (PCC) and peak signal-to-noise ratio (PSNR) values of 0.99 and 46.7, respectively. Compared to other works, our work focuses on the conjugate superimposed of perfect vortex beam (PVB), offering valuable insights into the beneficial aspects of vortex optical communication in long-distance transmission, interference resistance, and enhanced data transfer performance.