The feedback mechanism method of simulating the biological vision system has not been widely used in deep learning dehazing algorithms. To alleviate the difficulty of feature interaction, we combine the feedback mechanism with dense skip connections to fuse features of different levels in a dehazing network. Inspired by the feedback network in which previous network layers can have access to rich information processed by the following network layers, we propose an end-to-end dense feedback network (DFBDehazeNet) for single image dehazing that implements the feedback mechanism using hidden states of constrained RNN. The low-level hazy feature information can be continuously corrected by the high-level feature information obtained from the dense feedback block via the recurrent feedback connection. The top-down feedback mechanism is adopted in DFBDehazeNet to refine the low-level hazy feature information, thereby achieving a powerful image restoration effect. The ablation experiment proves that the iterative structure of DFBDehazeNet and the projection unit play an important role in removing haze from images. The experimental results show that the results of image haze removal are superior to the great majority of existing methods both qualitatively and quantitatively.