首页|Findings on Machine Learning Detailed by Investigators at Lund University (Reusa bility Report: Unpaired Deep-learning Approaches for Holographic Image Reconstru ction)
Findings on Machine Learning Detailed by Investigators at Lund University (Reusa bility Report: Unpaired Deep-learning Approaches for Holographic Image Reconstru ction)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Lund, Sweden, by NewsRx editors, r esearch stated, “Deep-learning methods using unpaired datasets hold great potent ial for image reconstruction, especially in biomedical imaging where obtaining p aired datasets is often difficult due to practical concerns. A recent study by L ee et al. (Nature Machine Intelligence 2023) has introduced a parameterized phys ical model (referred to as FMGAN) using the unpaired approach for adaptive holog raphic imaging, which replaces the forward generator network with a physical mod el parameterized on the propagation distance of the probing light.”
LundSwedenEuropeEmerging Technolog iesMachine IntelligenceMachine LearningLund University
NETL
NSTL
