Deep learning-based protocols to enhance infrared imaging systems
Deep learning-based protocols to enhance infrared imaging systems
复制标题
DOI：
10.1016/j.chemolab.2021.104390
复制
发表时间：
2021-07-31
影响因子：
3.9
通讯作者：
Bhargava, Rohit
中科院分区：
文献类型：
--
作者：
Falahkheirkhah, Kianoush;Yeh, Kevin;Bhargava, Rohit
红外（IR）光谱成像提供了形态和化学细节;然而，获得这种广泛的光谱空间信息需要能够快速记录高质量的数据。使用点扫描显微镜的离散频率红外（DFIR）成像在数据质量和采集速度之间取得了平衡，原则上，可以进一步通过计算方法进行辅助。在这里，我们报告了一个基于深度学习的框架，以补充数据采集和信息提取的过程。首先，我们引入了一个卷积神经网络（CNN）来利用空间和光谱信息将数据分割成信息丰富的子类，我们称之为IR-SEG网络。我们表明，该框架通过使用大约一半的特征来提高精度，这些特征用于IR数据的典型像素分类。其次，我们提出了一种基于生成对抗网络（GAN）的方法来重建完整的数据集，从不完整的空间和光谱数据记录中获得低损失的信息。对于典型的生物医学样品，这种方法被称为IR-REC，可以将数据采集速度提高20倍。除了提高数据的速度和质量，我们还提出了一种方法，利用互补形态细节来估计超出衍射极限的单波段红外图像的空间细节。最后，我们讨论了潜在的陷阱和新的机会，可以通过进一步发展这些方法来解决。总之，这些深度学习技术为红外成像提供了新的功能，可以更快地提取更高质量的信息。
Infrared (IR) spectroscopic imaging provides both morphologic and chemical detail; however, obtaining this extensive spectral-spatial information requires the ability to rapidly record high-quality data. Discrete frequency infrared (DFIR) imaging using a point scanning microscope strikes a balance between data quality and acquisition speed that, in principle, can further be aided by computational methods. Here, we report a deep learning-based framework to complement the process of data acquisition and information extraction. First, we introduce a convolutional neural network (CNN) to leverage both spatial and spectral information for segmenting data into informative sub-classes, which we call the IR-SEG network. We show that this framework increases accuracy by using approximately half of the features used in the typical pixel-wise classification of IR data. Second, we present a generative adversarial network (GAN)-based approach to reconstruct full data sets with low loss in the information from incomplete spatial and spectral data recording. Termed IR-REC, this approach is shown to speed up data acquisition by up to 20-fold for typical biomedical samples. In addition to enhancing the speed and quality of data, we also propose a method to utilize complementary morphologic detail to estimate the spatial details of a single band IR image beyond the diffraction limit. Finally, we discuss potential pitfalls and new opportunities that can be addressed by developing these methods further. Together, these deep learning techniques provide new capabilities for IR imaging to extract better quality information faster.