Brain MR image super-resolution via a deep convolutional neural network with multi-unit upsampling learning

Image super-resolution (SR) is a preferred approach to achieving high-resolution MR images because they are typically achieved in real world at the expense of reduced signal-to-noise ratio and/or increased imaging time. A novel deep residual network (DRN) with multi-unit upsampling learning is designed for MR image SR. The multi-unit upsampling learning mechanism involves multi-unit upsampling and adaptive learning. The designed DRN performs the SR task in the LR space to accelerate the network via an upsampling strategy at the late stage of the network architecture. A multi-unit upsampling strategy is proposed to transmit lost information in each residual unit and to accumulate the upscaled feature maps achieved by different residual units. An adaptive learning strategy following multi-unit upsampling is utilized to potentially discover the contributions of these upscaled feature maps to high-resolution MR image reconstruction by adaptively assigning different significance weights to the intermediate predictions. The proposed DRN achieves a fair good reconstruction performance, which is superior to some state-of-the-art deep-learning-based methods.