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Single-pixel imaging 12 years on: a review
Modern cameras typically use an array of millions of detector pixels to capture images. By
contrast, single-pixel cameras use a sequence of mask patterns to filter the scene along with …
contrast, single-pixel cameras use a sequence of mask patterns to filter the scene along with …
Far-field super-resolution ghost imaging with a deep neural network constraint
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel
measurements and thus has great potential in applications in various fields ranging from …
measurements and thus has great potential in applications in various fields ranging from …
Advances in quantum imaging with machine intelligence
Quantum imaging exemplifies the fascinating and counter‐intuitive nature of the quantum
world, where non‐local correlations are exploited for imaging of objects by remote and non …
world, where non‐local correlations are exploited for imaging of objects by remote and non …
Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging
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 …
usually require to experimentally collect a large set of labeled data to train a neural network …
Imaging through unknown scattering media based on physics-informed learning
Imaging through scattering media is one of the hotspots in the optical field, and impressive
results have been demonstrated via deep learning (DL). However, most of the DL …
results have been demonstrated via deep learning (DL). However, most of the DL …
DeepGhost: real-time computational ghost imaging via deep learning
The potential of random pattern based computational ghost imaging (CGI) for real-time
applications has been offset by its long image reconstruction time and inefficient …
applications has been offset by its long image reconstruction time and inefficient …
Compressive ghost imaging through scattering media with deep learning
Imaging through scattering media is challenging since the signal to noise ratio (SNR) of the
reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high …
reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high …
Computational ghost imaging based on an untrained neural network
Ghost imaging based on deep learning (DLGI) usually employs a supervised learning
strategy, and needs a large set of labeled data to train a neural network. However, in many …
strategy, and needs a large set of labeled data to train a neural network. However, in many …
Deep-learning denoising computational ghost imaging
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 …
a clear object image with a sub-Nyquist sampling ratio. We develop an end-to-end deep …
Practical advantage of quantum machine learning in ghost imaging
Demonstrating the practical advantage of quantum computation remains a long-standing
challenge whereas quantum machine learning becomes a promising application that can be …
challenge whereas quantum machine learning becomes a promising application that can be …