Abstract Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. However, the application of ML to microscopic images is limited as microscopic images are often 3D/4D, that is, the image sizes can be very large, and the images may suffer from serious noise generated due to optics. In this review, three types of feature reconstruction applications to microscopic images are discussed, which fully utilize the recent advancements in ML technologies. First, multi-frame super-resolution is introduced, based on the formulation of statistical generative model-based techniques such as Bayesian inference. Second, data-driven image restoration is introduced, based on supervised discriminative model-based ML technique. In this application, CNNs are demonstrated to exhibit preferable restoration performance. Third, image segmentation based on data-driven CNNs is introduced. Image segmentation has become immensely popular in object segmentation based on electron microscopy (EM); therefore, we focus on EM image processing.

中文翻译：

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基于生成和判别模型的显微图像恢复和分割方法


摘要 图像处理是最新机器学习（ML）技术最重要的应用之一。卷积神经网络 (CNN) 是一种流行的基于深度学习的 ML 架构，专为图像处理应用而开发。然而，机器学习在显微图像中的应用受到限制，因为显微图像通常是 3D/4D，即图像尺寸可能非常大，并且图像可能会受到光学产生的严重噪声的影响。在这篇综述中，讨论了三种类型的显微图像特征重建应用，它们充分利用了机器学习技术的最新进展。首先，基于统计生成模型技术（例如贝叶斯推理）的制定，引入多帧超分辨率。其次，介绍了基于监督判别模型的机器学习技术的数据驱动图像恢复。在此应用中，CNN 被证明具有更好的恢复性能。第三，介绍了基于数据驱动的 CNN 的图像分割。图像分割在基于电子显微镜（EM）的对象分割中变得非常流行；因此，我们重点关注电磁图像处理。