A novel, to the best of our knowledge, color computational ghost imaging scheme is presented for the reconstruction of a color object image, which greatly simplifies the experimental setup and shortens the acquisition time. Compared to conventional schemes, it only adopts one digital light projector to project color speckles and one single-pixel detector to receive the light intensity, instead of utilizing three monochromatic paths separately and synthesizing the three branch results. Severe noise and color distortion, which are common in ghost imaging, can be removed by the utilization of a generative adversarial network, because it has advantages in restoring the image’s texture details and generating the image’s match to a human’s subjective feelings over other generative models in deep learning. The final results can perform consistently better visual quality with more realistic and natural textures, even at the low sampling rate of 0.05.

We present a new color computational ghost imaging strategy using a sole single-pixel detector and training by simulated dataset, which can eliminate the actual workload of acquiring experimental training datasets and reduce the sampling times for imaging experiments. First, the relative responsibility of the color computational ghost imaging device to different color channels is experimentally detected, and then enough data sets are simulated for training the neural network based on the response value. Because the simulation process is much simpler than the actual experiment, and the training set can be almost unlimited, the trained network model has good generalization. In the experiment with a sampling rate of only 4.1%, the trained neural network model can still recover the image information from the blurry ghost image, correct the color distortion of the image, and get a better reconstruction result. In addition, with the increase in the sampling rate, the details and color characteristics of the reconstruction result become better and better. Feasibility and stability of the proposed method have been verified by the reconstruction results of the trained network model on the color objects of different complexities.

No abstract

An encryption method based on computational ghost imaging (CGI) with chaotic mapping and DNA encoding is proposed. To reduce the amount of keys in the CGI-based encryption system, the chaotic mapping algorithm is used to generate the random sequence as the speckle measurement matrix of CGI system. The measurement data of the bucket detector is subjected to block and DNA operations, which introduce the nonlinear characteristics in the encryption process. The problem of linear vulnerability of the encryption system has been greatly improved. Numerical simulation results show that, compared with the traditional CGI-based encryption method, the proposed method greatly reduces the amount of keys, increases the key space and enhances the security of the system.

No abstract