Abstract
Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography ({\mu}CT) images of sandstone and carbonate rocks. Digital rock imaging is limited by the capability of the scanning device resulting in trade-offs between resolution and field of view, and super resolution methods tested in this study aim to compensate for these limits. SRCNN models SR-Resnet, Enhanced Deep SR (EDSR), and Wide-Activation Deep SR (WDSR) are used on the Digital Rock Super Resolution 1 (DRSRD1) Dataset of 4x downsampled images, comprising of 2000 high resolution (800x800) raw micro-CT images of Bentheimer sandstone and Estaillades carbonate. The trained models are applied to the validation and test data within the dataset and show a 3-5 dB rise in image quality compared to bicubic interpolation, with all tested models performing within a 0.1 dB range. Difference maps indicate that edge sharpness is completely recovered in images within the scope of the trained model, with only high frequency noise related detail loss. We find that aside from generation of high-resolution images, a beneficial side effect of super resolution methods applied to synthetically downgraded images is the removal of image noise while recovering edgewise sharpness which is beneficial for the segmentation process. The model is also tested against real low-resolution images of Bentheimer rock with image augmentation to account for natural noise and blur. The SRCNN method is shown to act as a preconditioner for image segmentation under these circumstances which naturally leads to further future development and training of models that segment an image directly. Image restoration by SRCNN on the rock images is of significantly higher quality than traditional methods and suggests SRCNN methods are a viable processing step in a digital rock workflow.
Abstract (translated)
将基于超分辨率卷积神经网络（SRCNN）的单图像超分辨率（SISR）技术应用于砂岩和碳酸盐岩的微计算机层析成像（MU CT）。数字岩石成像受到扫描设备能力的限制，导致分辨率和视场之间的权衡，本研究中测试的超分辨率方法旨在补偿这些限制。SRCNN模型sr resnet、增强深sr（edsr）和宽激活深sr（wdsr）用于4x采样图像的数字岩石超分辨率1（drsrd1）数据集，包括2000个Bentheimer砂岩和Estaillades碳酸盐的高分辨率（800x800）原始显微CT图像。将训练模型应用于数据集内的验证和测试数据，与双三次插值相比，图像质量提高了3-5 dB，所有测试模型在0.1 dB范围内执行。差分图表明，在训练模型的范围内，图像的边缘锐度完全恢复，只有高频噪声相关的细节损失。我们发现，除了生成高分辨率图像外，用于综合降级图像的超分辨率方法的一个有益的副作用是在恢复边缘锐度的同时去除图像噪声，这有利于分割过程。该模型还针对本特海默岩石的真实低分辨率图像进行了测试，并对图像进行了增强，以考虑自然噪声和模糊。在这种情况下，SRCNN方法作为图像分割的先决条件，自然会导致直接分割图像的模型的进一步发展和训练。用SRCNN对岩石图像进行图像恢复，其质量明显高于传统方法，表明SRCNN方法是数字岩石工作流程中一个可行的处理步骤。
URL
https://arxiv.org/abs/1904.07470
