High dynamic range (HDR) imaging is to reconstruct high-quality images with a broad range of illuminations from a set of differently exposed images. Some existing algorithms align the input images before merging them into an HDR image, but artifacts of the registration appear due to misalignment. Recent works try to remove the ghosts by detecting motion region or skipping the registered process, however, the result still suffers from ghost artifacts for scenes with significant motions. In this paper, we propose a novel Multi-scale Channel Attention guided Network (MCANet) to address the ghosting problem. We use multi-scale blocks consisting of dilated convolution layers to extract informative features. The channel attention blocks suppress undesired components and guide the network to refine features to make full use of feature maps. The proposed MCANet recovers the occluded or saturated details and reduces artifacts due to misalignment. Experiments show that the proposed MCANet can achieve state-of-the-art quantitative and qualitative results.