We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiority of our W-shaped self-supervised computational ghost imaging (WSCGI) method through numerical simulations and experimental validations. Our results underscore the potential of self-supervised learning in advancing ghost imaging.

Ghost imaging (GI) has been received more and more attention in past thirty years. However, it has some limitations in the resolution. In this paper, we propose a scheme to improve the resolution of GI by a new imaging information, i.e. the coefficient of skewness coming from the fluctuations of the second-order correlation function, and put forward an enhanced GI protocol exploiting those fluctuations. We demonstrate experimentally that the additional information obtained from the new information makes it possible to improve resolution compared to the traditional GI scheme.

A multi-channel computational ghost imaging (GI) method based on multi-scale speckle optimization is proposed. We not only reduce imaging time and enhance imaging quality but also reduce interference among different channels. Using one bucket detector to receive total light intensity, the color speckle is formed by combining components obtained through the singular value decomposition of three self-designed multi-scale measurement matrices. Simulation and experimental results demonstrate that our designed method contributes to reducing imaging time and enhancing imaging quality, achieving improved visual quality even at low sampling rates. This approach enhances GI flexibility and holds potential for diverse applications, including target recognition and biomedical imaging.

To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the differences in light intensity distribution and sampling characteristics between Hadamard and 2D-Haar wavelet illumination patterns, we discovered that the lateral and longitudinal information detected by the high-frequency 2D-Haar wavelet measurement basis could be used to predictively adjust the diagonal measurement basis, thereby reducing the number of measurements required. Simulation and experimental results indicate that this scheme can still achieve high-quality imaging results with about a 25% reduction in the number of measurements. This approach provides a new perspective for enhancing the efficiency of computational ghost imaging.

The resolution is an important factor in evaluating image quality. In general, the resolution of correlation imaging is taken to the full width at half maximum (FWHM) of the point spread function (PSF) produced by the second-order correlation function. In this paper, we show that the resolution of correlation imaging can be improved by the fluctuation characteristic of the second-order correlation function. It is demonstrated both experimentally and theoretically that the resolution of the system can be drastically improved. We also prove that the FWHM of the PSF can be narrowed by $\sqrt{2^n}$ by extracting the n-order fluctuation information of the second-order correlation function.