Complex field imaging, which captures both the amplitude and phase information of input
optical fields or objects, can offer rich structural insights into samples, such as their …

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts
from input images. In general, digital image denoising algorithms, executed on computers …

Optical imaging and sensing systems based on diffractive elements have seen massive
advances over the last several decades. Earlier generations of diffractive optical processors …

Optical imaging has traditionally relied on hardware to fulfill its imaging function, producing
output measures that mimic the original objects. Developed separately, digital algorithms …

The rapid development of artificial general intelligence (AGI) introduces significant
performance challenges for next-generation computing. Electronic devices such as graphics …

Large‐scale and high‐dimensional permutation operations are important for various
applications in, for example, telecommunications and encryption. Here, all‐optical diffractive …

The 2024 Nobel Prize in Physics recognized John Hopfield and Geoffrey Hinton for their
pioneering work on artificial neural networks, which profoundly impacted the physical …

In many combustion systems, data collection through optical windows is often hindered by
fixed mechanical components that limit the number of available observation angles. This …

Generative models cover various application areas, including image, video and music
synthesis, natural language processing, and molecular design, among many others. As …

Efficiently tracking and imaging interested moving targets is crucial across various
applications, from autonomous systems to surveillance. However, persistent challenges …