In this manuscript, we propose a novel framework of computational ghost imaging, ie, ghost
imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI …

Artificial intelligence (AI) techniques such as deep learning (DL) for computational imaging
usually require to experimentally collect a large set of labeled data to train a neural network …

We propose a deep learning denoising computational ghost imaging (CGI) method to obtain
a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep …

In this Letter, we propose a learning-based correction method to realize ghost imaging (GI)
through dynamic scattering media using deep neural networks with Gaussian constraints …

Phase information and spatially coherent illumination have usually been considered
indispensable components of most microwave imaging systems. Dynamic metasurface …

Correlation is widely used to reconstruct the object image in ghost imaging (GI). But it only
offers a linear proportion of the signal-to-noise ratios (SNR) to the number of measurements …

We present a new technique to denoise ghost imaging (GI) in which conventional intensity
correlation GI and an iteration process have been combined to give an accurate estimate of …

Sunlight background noise significantly hinders the operation of ghost imaging systems,
posing a considerable challenge for target imaging under daytime sunlight conditions. This …

In this paper, we introduce speckle-shifting ghost imaging (SSGI) which uses several
corresponding shifted groups of speckle patterns instead of random speckle patterns in …

Imaging and edge detection have been widely applied and played an important role in
security checking and medical diagnosis. However, as we know, most edge detection based …