Residual Attention Network for Wavelet Domain Super-Resolution

Single-image super-resolution plays an important role in computer vision area. However, previous works using convolutional neural networks perform badly when reconstructing high frequency details, result in over-smooth and lacking of textural information in the output. At the same time, super-resolution computation always relays on convolutional neural networks with huge depth, which is super tricky to train and use. In this paper, we propose a novel network with better textural details in wavelet domain, which is composed of a feature extract layer, residual channel attention groups (RCAG) and a residual up-sampling layer based on inverse discrete wavelet transform. Channel attention and spatial attention layers are inserted into residual channel and spatial attention blocks (RCSAB), enhancing the learning of high frequency information with attention maps. Composed of a chain of RCSAB and a channel attention layer with short skip connection, RCAG is good at catching long-term high frequency information. Then the feature mapping component is composed of a chain of RCAG. Experiment shows that our method performs better than state-of-the-art methods on benchmark datasets in different scales.