Ghost imaging incorporating deep learning technology has recently attracted much attention in the optical imaging field. However, deterministic illumination and multiple exposure are still essential in most scenarios. Here we propose a ghost imaging scheme based on a novel dynamic decoding deep learning framework (Y-net), which works well under both deterministic and indeterministic illumination. Benefited from the end-to-end characteristic of our network, the image of a sample can be achieved directly from the data collected by the detector. The sample is illuminated only once in the experiment, and the spatial distribution of the speckle encoding the sample in the experiment can be completely different from that of the simulation speckle in training, as long as the statistical characteristics of the speckle remain unchanged. This approach is particularly important to high-resolution x-ray ghost imaging applications due to its potential for improving image quality and reducing radiation damage.

A spatial multiplexing reconstruction method has been proposed to improve the sampling efficiency and image quality of Fourier-transform ghost imaging. In this method, the sensing equation of Fourier-transform ghost imaging is established based on recombination and reutilization of the correlated intensity distributions of light fields. It is theoretically proved that the scale of the sensing matrix in the sensing equation can be greatly reduced, and spatial multiplexing combined with this matrix reduction provides the feasibility of ghost imaging with just a few measurements. Experimental results show better visibility and signal-to-noise ratio in the Fourier spectrums reconstructed via spatial multiplexing compared with previous methods. The transmittance of an object is also recovered in spatial domain with better image quality based on its spectrum of spatial multiplexing reconstruction. This method is especially important to x-ray ghost imaging applications due to its potential for reducing radiation damage and achieving high quality images in x-ray microscopy.

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