FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution

Most current image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) use residual learning in network structural design, which contributes to effective back propagation, thus improving SR performance by increasing model scale. However, deep residual network suffers some redundancy in model representational capacity by introducing short paths, thus hindering the full mining of model capacity. In addition, blindly enlarging the model scale will cause more problems in model training, even with residual learning. In this work, a novel network architecture is introduced to fully exploit the representational capacity of the model, where all skip connections are implemented by weighted channel concatenation, followed by a 1$\times$1 conv layer. Based on this weighted skip connection, we construct the building modules of our model, and improve the global feature fusion (GFF). Unlike most previous models, all skip connections in our network are channel-concatenated and no residual connection is adopted. It is therefore termed as fully channel-concatenated network (FC$^2$N). Due to the full exploitation of model capacity, the proposed FC$^2$N achieves better performance than other advanced models with fewer model parameters. Extensive experiments demonstrate the superiority of our method to other methods, in terms of both quantitative metrics and visual quality.

Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network

Spatial resolution is a critical imaging parameter in magnetic resonance imaging (MRI). Acquiring high resolution MRI data usually takes long scanning time and would subject to motion artifacts due to hardware, physical, and physiological limitations. Single image super-resolution (SISR), especially that based on deep learning techniques, is an effective and promising alternative technique to improve the current spatial resolution of magnetic resonance (MR) images. However, the deeper network is more difficult to be effectively trained because the information is gradually weakened as the network deepens. This problem becomes more serious for medical images due to the degradation of training examples. In this paper, we present a novel channel splitting and serial fusion network (CSSFN) for single MR image super-resolution. Specifically, the proposed CSSFN network splits the hierarchical features into a series of subfeatures, which are then integrated together in a serial manner. Thus, the network becomes deeper and can deal with the subfeatures on different channels discriminatively. Besides, a dense global feature fusion (DGFF) is adopted to integrate the intermediate features, which further promotes the information flow in the network. Extensive experiments on several typical MR images show the superiority of our CSSFN model over other advanced SISR methods.

Channel Splitting Network for Single MR Image Super-Resolution

High resolution magnetic resonance (MR) imaging is desirable in many clinical applications due to its contribution to more accurate subsequent analyses and early clinical diagnoses. Single image super resolution (SISR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. In the past few years, SISR methods based on deep learning techniques, especially convolutional neural networks (CNNs), have achieved state-of-the-art performance on natural images. However, the information is gradually weakened and training becomes increasingly difficult as the network deepens. The problem is more serious for medical images because lacking high quality and effective training samples makes deep models prone to underfitting or overfitting. Nevertheless, many current models treat the hierarchical features on different channels equivalently, which is not helpful for the models to deal with the hierarchical features discriminatively and targetedly. To this end, we present a novel channel splitting network (CSN) to ease the representational burden of deep models. The proposed CSN model divides the hierarchical features into two branches, i.e., residual branch and dense branch, with different information transmissions. The residual branch is able to promote feature reuse, while the dense branch is beneficial to the exploration of new features. Besides, we also adopt the merge-and-run mapping to facilitate information integration between different branches. Extensive experiments on various MR images, including proton density (PD), T1 and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.