BERKELEY, Calif., July 26, 2017 — A data-driven image colorization technique uses AI and deep learning to colorize images in real-time, providing even novices with the ability to quickly colorize gray scale images with satisfactory results. The system is guided by a Convolutional Neural Network (CNN), which propagates user edits by fusing low-level cues with high-level semantic information learned from large-scale data. The colorization is performed in a single feed-forward pass, enabling real-time use. The CNN is trained to directly map gray scale images, along with user inputs, to the output colorization. The user...Read full articleRelated content from Photonics MediaARTICLES‘Crystal Ball’ VR System Supports Collaborative TasksResearchers at the University of British Columbia and the University of Saskatchewan have developed a ball-shaped VR display that supports up to two users at a time, using advanced calibration and...BioPhotonics April 2019 IssueNanostructured Plasmonic Materials Could Provide More Natural Color for DisplaysUltralow-power, ultrahigh-definition displays and screens that will be easier on the eyes could be possible with technology being developed by researchers at the University of Central Florida (UCF)....Photonics.com 6/9/2020Photon Sieve Used to Display Holographic Images with Wide Viewing AngleResearchers at the Korea Advanced Institute of Science and Technology (KAIST) have designed an ultrathin display that can project dynamic, multicolored holographic images with a wide viewing angle....Photonics.com 4/22/2019Research Team Discovers How to Create and Control Structural ColorResearchers from Pennsylvania State University (Penn State) and the Massachusetts Institute of Technology (MIT) have shown how a clear surface covered in transparent droplets and lit with a single...Photonics Spectra May 2019 IssuePRODUCTSPortable ProjectorsThe PicoPix Micro 2 and PicoPix Max One portable projectors from Philips are designed for consumer streaming and gaming. 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