Image super-resolution network based on a multi-branch attention mechanism

As a member of low-level visual tasks, image super-resolution (SR) is now mostly implemented by deep learning. Although the deeper convolution neural network can bring larger receptive field, it will increase the amount of calculation, make the training difficult and reduce efficiency. In addition, the feature information obtained by each channel plays a different and important role in the detail recovery during the SR process. To settle the above problems and improve the performance, we develop a multi-branch attention SR model. The main network contains multiple residual bodies, which are composed of several residual units. Additionally, we construct a multi-branch attention mechanism, which divides all channels into equal parts. Then, the network learns the relationship between channels and focuses more on the high-frequency feature channels. Experimental results show that the proposed algorithm is superior to the state-of-the-art algorithms in terms of subjective visual quality and objective evaluation criteria.