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. We propose a Texture and Attention Guided Generative Adversarial Network (TAGAN) for better 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.