Rivenson Yair, Wu Yichen, Ozcan Aydogan
1Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
2Bioengineering Department, University of California, Los Angeles, CA 90095 USA.
Light Sci Appl. 2019 Sep 11;8:85. doi: 10.1038/s41377-019-0196-0. eCollection 2019.
Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.
深度学习的最新进展催生了一种具有实时性能的全息图像重建和相位恢复技术的新范式。通过数据驱动的方法，这些新兴技术克服了与现有全息图像重建方法相关的一些挑战，同时也将全息术的硬件要求降至最低。这些最新进展为相干成像系统在生物医学和工程研究及相关应用中的使用开辟了无数新机会。
