Researchers from KAIST and UC Berkeley have developed a neural network-based method to correct optical distortions in deep tissue microscopy without additional hardware. The system uses Neural Fields ...

Researchers have published a programmable framework that overcomes a key computational bottleneck of optics-based artificial intelligence systems. In a series of image classification experiments, they ...

The deep neural network models that power today's most demanding machine-learning applications are pushing the limits of traditional electronic computing hardware, according to scientists working on a ...

Researchers from Skoltech and the Shanghai Institute of Optics and Fine Mechanics have developed an approach that helps ...

As part of this week's SPIE Optics & Photonics conference program Emerging Topics in Artificial Intelligence, Asst. Prof. Logan G. Wright, of Yale University, presented an invited paper, entitled ...

Professor Iksung Kang, KAIST >Observing the depths of a living brain with clarity has traditionally required expensive, high-end equipment. However ...

Fig. 1 Schematic illustration of the DFAMFPP principle: Multiple dual-frequency fringe patterns are overlaid in a single long-exposure image. A deep neural network then reconstructs time-resolved 3D ...

Fiber-optic technology revolutionized the telecommunications industry and may soon do the same for brain research. A group of researchers from Washington University in St. Louis in both the McKelvey ...

Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...

Fig. 1 Neural array imaging system and its principle behind its ability to break the trade-off between aperture size, ffeld of view, and imaging quality. (a) The system consists of a metalens array, a ...