TAGAN: Texture and Attention Guided Generative Adversarial Network for Image Super Resolution

Haitao Wang, Jiande Sun, Wenxiu Diao, Jing Li, Kai Zhang

Abstract: Super Resolution (SR) methods based on Generative Adversarial Networks (GANs) accomplish predominant execution in visual perception and image quality. These methods are mainly generated by traditional Peak-Signal-to-Noise-Ratio (PSNR)oriented or perceptual-driven. As the reconstruction process usually loses high frequency information, various methods aim to preserve more details. To make the details of the generated image richer, the Gradient Weight (GW) loss is introduced in the proposed method, because the gradient can reflect the texture of the image to a certain extent. The GW loss function is helpful to improve the edge and detailed texture of the generated image. Furthermore, we introduce attention mechanism to the image reconstruction block via Squeeze and Excitation Net (SENet). Attention mechanism can effectively aggregate the global features obtained by the nonlinear mapping network, and improve the channel sensitivity of the model. With the help of GW and attention mechanism, the proposed method can achieve better performance and visual quality in image texture detail restoration. The performance comparison between the state-of-the-art methods and our proposed method verifies the feasibility and reliability of the proposed method.