  Computed Tomography (CT) imaging technique is widely used in geological
exploration, medical diagnosis and other fields. In practice, however, the
resolution of CT image is usually limited by scanning devices and great
expense. Super resolution (SR) methods based on deep learning have achieved
surprising performance in two-dimensional (2D) images. Unfortunately, there are
few effective SR algorithms for three-dimensional (3D) images. In this paper,
we proposed a novel network named as three-dimensional super resolution
convolutional neural network (3DSRCNN) to realize voxel super resolution for CT
images. To solve the practical problems in training process such as slow
convergence of network training, insufficient memory, etc., we utilized
adjustable learning rate, residual-learning, gradient clipping, momentum
stochastic gradient descent (SGD) strategies to optimize training procedure. In
addition, we have explored the empirical guidelines to set appropriate number
of layers of network and how to use residual learning strategy. Additionally,
previous learning-based algorithms need to separately train for different scale
factors for reconstruction, yet our single model can complete the multi-scale
SR. At last, our method has better performance in terms of PSNR, SSIM and
efficiency compared with conventional methods.
