Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Abstract

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.

An Improved GAN-Based Image Restoration Method for Imaging Logging Images

“…The CA aims to model the importance of each channel in the network extraction feature map according to the interdependencies between the acquired channels, so that different features can be suppressed or enhanced for different tasks, and its basic structure is shown in Figure 2. CA for the input features of the image can be extracted from the global perceptual field, and the channels of the image are compressed by means of a compression mode performing an average pooling operation in the channel dimension, and the process can be represented by Equation (7).…”

“…CNN supervised learning has been widely used in computer vision applications. In contrast, unsupervised learning based on CNNs has received less attention [7]. Therefore, Radford et al proposed a class of deep learning models called deep convolutional generative adversarial networks with certain structural constraints and a powerful algorithmic model for unsupervised learning, which was tested with good results from different perspectives on the model networks.…”

4× Super‐resolution of unsupervised CT images based on GAN

“…Their approach lost only some high‐frequency details while recovering edge sharpness more effectively. Hu [19], Wang [20], and Shan [21] used the residual channel attention network structure of RCAN to perform super‐resolution analysis of CT images using a multi‐scale attention block with multiple branches that automatically generated weights to adjust the network.…”