Image captioning is an important task, which connects computer vision and natural language processing, two major artificial intelligence fields. In recent years, encoder-decoder frameworks integrated with attention mechanism have made significant process in captioning. However, many attention-based methods only use spatial attention mechanism. In this study, we propose a novel dual refined attention model for image captioning. In the proposed model, we use not only spatial attention but also channel-wise attention and then use a refine module to refine the image features. By using the refine module, the proposed model can filter the redundant and irrelevant features in the attended image features. We validate the proposed model on MSCOCO dataset via various evaluation metrics, and the results show the effectiveness of the proposed model.