  Deep learning-based super-resolution (SR) techniques have generally achieved
excellent performance in the computer vision field. Recently, it has been
proven that three-dimensional (3D) SR for medical volumetric data delivers
better visual results than conventional two-dimensional (2D) processing.
However, deepening and widening 3D networks increases training difficulty
significantly due to the large number of parameters and small number of
training samples. Thus, we propose a 3D convolutional neural network (CNN) for
SR of medical volumetric data called ParallelNet using parallel connections. We
construct a parallel connection structure based on the group convolution and
feature aggregation to build a 3D CNN that is as wide as possible with few
parameters. As a result, the model thoroughly learns more feature maps with
larger receptive fields. In addition, to further improve accuracy, we present
an efficient version of ParallelNet (called VolumeNet), which reduces the
number of parameters and deepens ParallelNet using a proposed lightweight
building block module called the Queue module. Unlike most lightweight CNNs
based on depthwise convolutions, the Queue module is primarily constructed
using separable 2D cross-channel convolutions. As a result, the number of
network parameters and computational complexity can be reduced significantly
while maintaining accuracy due to full channel fusion. Experimental results
demonstrate that the proposed VolumeNet significantly reduces the number of
model parameters and achieves high precision results compared to
state-of-the-art methods.
