Currently, most image denoising models based on convolutional neural networks cannot fully utilize the redundancy of image data, which limits the expressive power of the models. Moreover, edge information is often used as a priori knowledge for effective denoising, while texture information is usually ignored. To address these issues, a new image denoising network is proposed, which firstly uses the attentional similarity module to extract global similarity features of the image, and smooths and suppresses the noise in the attentional similarity module through average pooling to further improve the network performance; secondly, the dilated residual module is used to extract both local and global features of the image; finally, a global residual learning is utilized to enhance the denoising performance from shallow to deep layers. In addition, a texture extraction network is designed to extract local binary patterns from noisy images to obtain texture information, which can be utilized as a priori knowledge to preserve the details in the evolved images during the denoising process. The experimental results show that compared with some advanced denoising networks, the newly proposed denoising network has a great improvement in image vision, higher efficiency and peak signal-to-noise ratio by about 2 dB, and structural similarity by about 3%, which is more conducive to practical applications.