A new deep learning method for image deblurring in optical microscopic systems.,Journal of Biophotonics

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A new deep learning method for image deblurring in optical microscopic systems.
Journal of Biophotonics ( IF 2.0 ) Pub Date : 2020-01-01 , DOI: 10.1002/jbio.201960147
Huangxuan Zhao _{1,

2,

3} , Ziwen Ke _{4,

5} , Ningbo Chen ₁ , Songjian Wang _{2,

3} , Ke Li _{1,

2,

3} , Lidai Wang ₆ , Xiaojing Gong ₁ , Wei Zheng ₁ , Liang Song ₁ , Zhicheng Liu _{2,

3} , Dong Liang ₄ , Chengbo Liu ₁

Affiliation

Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point‐spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such as slow processing time due to multiple iterations required to deblur and suboptimal in cases where the experimental operator chosen to represent PSF is not optimal. In this paper, we present a deep‐learning‐based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution. We compared our results against several existing deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre‐determined experimental operator. Our method has several advantages including simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

中文翻译：

一种用于光学显微镜系统中图像去模糊的新的深度学习方法。

反卷积是光学成像系统中最常用的图像处理方法，用于消除由点扩展函数（PSF）引起的模糊。尽管此方法已成功地实现了去模糊，但它具有几个缺点，例如，由于需要多次迭代来进行去模糊而导致处理时间较慢，并且在选择表示PSF的实验算子不是最优的情况下，处理效果欠佳。在本文中，我们提出了一种基于深度学习的去模糊方法，该方法快速且适用于光学显微成像系统。我们在公开数据，模拟数据和实验数据（包括2D光学显微镜数据和3D光声显微镜数据）上测试了建议的去模糊方法的鲁棒性，与去卷积相比，这些结果均显示出大大改善的去模糊结果。我们将我们的结果与几种现有的反卷积方法进行了比较。我们的结果优于传统技术，不需要多次迭代或预定的实验算子。我们的方法具有几个优点，包括操作简单，计算时间短，去模糊效果好以及在所有类型的光学显微成像系统中都有广泛的应用。深度学习方法为去模糊开辟了一条新途径，可以应用于各种生物医学成像领域。

更新日期：2020-01-01

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