Image super-resolution method using a generative adversarial network incorporating attention and residual density

Image super-resolution reconstruction is currently gaining huge attention because of its very promising applications. However, there are still problems such as loss of detailed features and low signal-to-noise ratio. To address this problem, we propose a generative adversarial network that incorporates attention and residual density. Firstly, the network makes full use of the different layers of features for fusion and prevents gradient decay through the residual dense module. Then through the attention gate structure, the noisy information is suppressed and the signal-to-noise ratio value is improved. The experimental results show that the method proposed is improved in peak signal-to-noise ratio and structural similarity index measure compared to other comparative methods, which proves the advancement of the method proposed.