Aiming at the problem that the existing single-image super-resolution methods tend to ignore the differences and relations between different structures and textures in the original image during the reconstruction process, resulting in the lack of texture details and artifacts in the generated high-resolution image, an Image Super-resolution Reconstruction Algorithm for Texture Details Recovery (TDRSR) is proposed.The method consists of gradient branch, texture branch and image super-resolution branch. Among them, the class attention module is used between the gradient branch and the texture branch to deal with the feature confusion problem of the two, and the mutual promotion of structural features and texture features is realized through the bidirectional feature fusion module, which is used as prior information to achieve texture details. purpose of information enhancement. In addition, the image super-resolution branch also helps the network to retain richer contextual information and texture details in the image by building a feature recovery module that utilizes both shallow and deep information. The method trains the network on the DIV2K dataset, and conducts test experiments on 5 benchmark test sets Set5, Set14, B100, Urban100 and MANGA109, the peak signal-to-noise ratio (PSNR): 37.88dB, 33.28dB, 32.0781dB, 31.89dB and 38.39dB, which are significantly improved compared to the results of other methods. The experimental results show that the method obtains effective reconstructed images and preserves more image details, generating super-resolution images with sharp edges and realistic details.