Fiber bundle image restoration using deep learning
Fiber bundle image restoration using deep learning
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DOI：
10.1364/ol.44.001080
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发表时间：
2019-03-01
期刊：
影响因子：
3.6
通讯作者：
Barnard, Kobus
中科院分区：
文献类型：
--
作者：
Shao, Jianbo;Zhang, Junchao;Barnard, Kobus
我们提出了一种基于深度学习的恢复方法来去除蜂窝图案并提高纤维束（FB）图像的分辨率。通过构建和校准双传感器成像系统，我们捕获FB图像和相应的地面真实数据来训练网络。没有纤维束固定模式的图像恢复从原始FB图像作为直接输入，和空间分辨率显着增强的训练样本类型。我们还构建了两种图像类型之间的亮度映射，以有效地使用所有数据，提供输出预期亮度图像的能力。我们评估我们的框架，从透镜组织和人类组织学标本，使用客观和主观的措施获得的数据。(c)2019美国光学学会
We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolution for fiber bundle (FB) images. By building and calibrating a dual-sensor imaging system, we capture FB images and corresponding ground truth data to train the network. Images without fiber bundle fixed patterns are restored from raw FB images as direct inputs, and spatial resolution is significantly enhanced for the trained sample type. We also construct the brightness mapping between the two image types for the effective use of all data, providing the ability to output images of the expected brightness. We evaluate our framework with data obtained from lens tissues and human histological specimens using both objective and subjective measures. (c) 2019 Optical Society of America