自动对焦
光学(聚焦)
计算机科学
人工智能
卷积(计算机科学)
卷积神经网络
特征(语言学)
人工神经网络
计算机视觉
模式识别(心理学)
光学
语言学
物理
哲学
作者
Yang Xiang,Zhujun He,Qing Liu,Jialin Chen,Yixiong Liang
出处
期刊:Ultramicroscopy
[Elsevier]
日期:2021-01-01
卷期号:220: 113146-113146
被引量:4
标识
DOI:10.1016/j.ultramic.2020.113146
摘要
During the process of whole slide imaging, it is necessary to focus thousands of fields of view to obtain a high-quality image. To make the focusing procedure efficient and effective, we propose a novel autofocus algorithm for whole slide imaging. It is based on convolution and recurrent neural networks to predict the out-of-focus distance and subsequently update the focus location of the camera lens in an iterative manner. More specifically, we train a convolution neural network to extract focus information in the form of a focus feature vector. In order to make the prediction more accurate, we apply a recurrent neural network to combine focus information from previous search iteration and current search iteration to form a feature aggregation vector. This vector contains more focus information than the previous one and is subsequently used to predict the out-of-focus distance. Our experiments indicate that our proposed autofocus algorithm is able to rapidly determine the optimal in-focus image. The code is available at https://github.com/hezhujun/autofocus-rnn.
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