CT-image of rock samples super resolution using 3D convolutional neural network

Computed Tomography (CT) imaging technique is widely used in geological exploration, medical diagnosis and other fields. However, the resolution of CT images is usually limited by scanning devices and cost. Super-resolution (SR) methods based on deep learning provide remarkable performance for two-dimensional (2D) images. Unfortunately, few effective SR algorithms are available for three-dimensional (3D) images. This study proposes a novel network named as three-dimensional super-resolution convolutional neural network (3DSRCNN) to realize voxel SR imaging of rock samples. To solve the practical problems faced in the training process, such as slow convergence of network training and insufficient memory, we utilized adjustable learning rate, residual-learning, gradient clipping, momentum stochastic gradient descent (SGD) strategy to optimize training procedure. In addition, we have explored the empirical guidelines to set an appropriate number of network layers. Previous learning-based algorithms need to separately train samples for different scale factors; by contrast, our single model can perform the multi-scale SR. Further, our proposed method provides better performance in terms of PSNR, SSIM and efficiency compared with conventional methods.