# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 1 Introduction  
Single-image super-resolution (SISR) is an important low-level computer-vision task with high practical value in many fields such as industrial inspection, medical imaging, and security monitoring. SISR aims at recovering a high-resolution image from only one low-resolution image. For this ill-posed inverse problem, widely used interpolation methods cannot achieve visually pleasing results and many learning-based methods [1; 2] have been proposed. In recent years, deep learning-based algorithms [3; 4; 5; 6; 7; 8; 9] have been developed which have greatly improved the quality, and the detail of the images can be better preserved with these powerful deep networks. The introduction of attention mechanisms further improves the representation power of the neural networks. SENet [10] focuses onthe attention between channels and achieves good results. Attention is not limited to channels; many other kinds of attention (e.g., spatial attention) play important roles in tasks of segmentation, detection, and re-id. However, most of the recent approaches in this task focus only on stacking deep networks to enhance the representation power of the models, which leads to large computation and memory cost. These methods ignore the use of attention mechanisms which can greatly reduce information redundancy and improve network efficiency. Therefore, we propose a powerful deep network with three different types of effective attention mechanisms to enhance the super-resolution task. The network structure is another important factor that influences the representation power of networks. In recent studies, residual networks with very deep structure [4] or networks with dense connections [8; 9] were found effective in super-resolution tasks, both of which have achieved good results. In order to make the networks more powerful, much recent research has focused on taking advantage of both the effective network topologies [7; 11]. In this work we introduced an attention-enhanced multi-kernel mixed-link network. In the proposed structure, the network was designed with synchronous residual and dense connection to gain contiguous memory [8] (CM) and make full use of the hierarchical information from the LR space. Also, we used different kernel size to help the network gain different receptive fields and kernel attention was introduced to fuse different outputs between convolutional layers to improve the network performance. Moreover, we add channel attention between the connections of the layers and spatial attention in the reconstruction networks for further filtering and enhancing the flow of gradient and information. When training the network, we introduced multi-supervise, which enables block-output high-resolution images, and we calculate the loss between all of the outputs and target to make TAN stably output high-resolution images with high quality. We summarize our main contributions in the following points:  
* We propose a triple-attention mixed-link network (TAN) for image super-resolution. The proposed three attention mechanisms provide 0.49 dB, 0.58 dB, and 0.32 dB performance gain when evaluating on Set5, Set14, and BSD100. * We propose an attention-enhanced multi-kernel mixed-link block which could help achieve better performance with 50% parameter growth rate compared with previous approaches. * Our model achieved state-of-the-art performance according to the benchmark. With the help of mixed-link topology, the model could gain better representation power than other methods. The channel, kernel, and spatial attention enable the network to select and fuse information to make the model more effective. Thanks to the effective attention and network topology, our proposed TAN could not only achieve better performance but also contain fewer parameters, which is of great practical value. In the next section, we mainly analyze the recent research related to this paper and analyze the strengths and problems of the previous methods.