Most denoising networks only perform well in the task of synthetic noise denoising, and only extract features from a single scale, which can not better reconstruct a clean image. To solve the above problems, this paper proposes a blind denoising algorithm for real noisy images based on multi-scale feature fusion. The horizontal network structure of the algorithm uses adaptive dense residual blocks to extract rich features of the same scale, and selectively enhance features with large amount of information. The vertical network structure uses pyramid layer and encode-decode to further obtain different receptive fields to realize multi-scale feature extraction, The full fusion of the features of the same horizontal scale and the features of different vertical scales is more conducive to noise removal and retain the edge details of the image. The proposed network is evaluated on the real noise test set (DND and SIDD). The peak signal-to-noise ratio (PSNR) is 39.62 and 39.49 respectively, and the structural similarity (SSIM) is 0.956 and 0.954 respectively. The experimental results show that the network proposed in this paper has achieved better performance.