Zhang Xiaoyu, Chen Yifan, Ning Kefu, Zhou Can, Han Yutong, Gong Hui, Yuan Jing
Opt Express. 2018 Nov 12;26(23):30762-30772. doi: 10.1364/OE.26.030762.
Current optical-sectioning methods require complex optical system or considerable computation time to improve imaging quality. Here we propose a deep learning-based method for optical sectioning of wide-field images. This method only needs one pair of contrast images for training to facilitate reconstruction of an optically sectioned image. The removal effect of background information and resolution that is achievable with our technique is similar to traditional optical-sectioning methods, but offers lower noise levels and a higher imaging depth. Moreover, reconstruction speed can be optimized to 14 Hz. This cost-effective and convenient method enables high-throughput optical sectioning techniques to be developed.
当前的光学切片方法需要复杂的光学系统或相当长的计算时间来提高成像质量。在此，我们提出一种基于深度学习的宽场图像光学切片方法。该方法仅需一对对比度图像进行训练，以利于光学切片图像重建。我们技术实现的背景信息去除效果和分辨率与传统光学切片方法相似，但具有更低的噪声水平和更高的成像深度。此外，重建速度可优化至14赫兹。这种经济高效且便捷的方法能够推动高通量光学切片技术的发展。
