论文标题
使用深度学习的单杆自动对焦图像
Single-shot autofocusing of microscopy images using deep learning
论文作者
论文摘要
我们展示了一种基于深层学习的自动对焦方法，称为Deep-R，该方法经过训练，可快速盲目自动对焦，这是在任意异常平面上获得的样品的单发显微镜图像。我们说明了使用荧光和Brightfield显微镜模态成像的各种组织切片的Deep-R的功效，并在不同的情况下（例如均匀的轴向散焦以及视野内的样品倾斜）在不同场景下自动进行了快照。我们的结果表明，与标准的在线算法自动关注方法相比，DEEP-R的速度明显更快。这个基于深度学习的盲目自动关注框架为大型样品区域快速微观成像开辟了新的机会，还减少了样品上的光子剂量。
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane. We illustrate the efficacy of Deep-R using various tissue sections that were imaged using fluorescence and brightfield microscopy modalities and demonstrate snapshot autofocusing under different scenarios, such as a uniform axial defocus as well as a sample tilt within the field-of-view. Our results reveal that Deep-R is significantly faster when compared with standard online algorithmic autofocusing methods. This deep learning-based blind autofocusing framework opens up new opportunities for rapid microscopic imaging of large sample areas, also reducing the photon dose on the sample.
