In order to obtain high quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention guided mechanism and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also apply the attention guided mechanism to filter the edge holding operation. Besides, we use the global residual network strategy to model the residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against state of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the image sharpness and detail performance, so as to obtain better recovery effect.