  Single image super resolution is of great importance as a low-level computer
vision task. Recent approaches with deep convolutional neural networks have
achieved im-pressive performance. However, existing architectures have
limitations due to the less sophisticated structure along with less strong
representational power. In this work, to significantly enhance the feature
representation, we proposed Triple Attention mixed link Network (TAN) which
consists of 1) three different aspects (i.e., kernel, spatial and channel) of
attention mechanisms and 2) fu-sion of both powerful residual and dense
connections (i.e., mixed link). Specifically, the network with multi kernel
learns multi hierarchical representations under different receptive fields. The
output features are recalibrated by the effective kernel and channel attentions
and feed into next layer partly residual and partly dense, which filters the
information and enable the network to learn more powerful representations. The
features finally pass through the spatial attention in the reconstruction
network which generates a fusion of local and global information, let the
network restore more details and improves the quality of reconstructed images.
Thanks to the diverse feature recalibrations and the advanced information flow
topology, our proposed model is strong enough to per-form against the
state-of-the-art methods on the bench-mark evaluations.
