Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images

by   Ying Da Wang, et al.

Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography (μCT) 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.


page 12

page 13

page 14

page 15

page 17

page 19

page 20

page 21


Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning

Digital Rock Imaging is constrained by detector hardware, and a trade-of...

Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN

Anime is a storytelling medium similar to movies and books. Anime images...

Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modelling

There are inherent field-of-view and resolution trade-offs in X-Ray micr...

A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution

X-ray micro-computed tomography (micro-CT) has been widely leveraged to ...

Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques

Diabetic foot ulcers (DFUs) constitute a serious complication for people...

CT-image Super Resolution Using 3D Convolutional Neural Network

Computed Tomography (CT) imaging technique is widely used in geological ...

Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network

Normalizing flow models have been used successfully for generative image...

Please sign up or login with your details

Forgot password? Click here to reset