In this paper, an image denoising network based on a hybrid attention mechanism and dual residual learning is proposed. The network uses a dual residual network learning structure based on different sizes of convolution kernels, which can not only reduce the difficulty of fitting deeper network structures, but also represent the multi-scale structure in the image. In the proposed denoising network, the feature channels are adaptively adjusted through hybrid local and non-local attention modules. Such hybrid attention module ensures that convolutional neural network can not only pay attention to the local features, but also depict the long-range dependencies in image. By comparing with several common deep denoising networks, the experimental results show that the proposed method can effectively suppress noise at different levels, specifically for the high-level noise.