With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

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 …

Most of the neural networks proposed so far for computational imaging (CI) in optics employ
a supervised training strategy, and thus need a large training set to optimize their weights …

With the explosive growth of mathematical optimization and computing hardware, deep
neural networks (DNN) have become tremendously powerful tools to solve many …

Single-pixel imaging (SPI) is a typical computational imaging modality that allows two-and
three-dimensional image reconstruction from a one-dimensional bucket signal acquired …

Low signal-to-noise ratio (SNR) measurements, primarily due to the quartic attenuation of
intensity with distance, are arguably the fundamental barrier to real-time, high-resolution …

Demonstrating the practical advantage of quantum computation remains a long-standing
challenge whereas quantum machine learning becomes a promising application that can be …

Imaging through nonstatic scattering media is one of the major challenges in optics, and
encountered in imaging through dense fog, turbid water, and many other situations. Here …

Optical cryptanalysis is essential to the further investigation of more secure optical
cryptosystems. Learning-based attack of optical encryption eliminates the need for the …