Recent dehazing networks learn more discriminative high-level features by designing deeper networks or introducing complicated structures, while ignoring inherent feature correlations in intermediate layers. In this article, we establish a novel and effective end-to-end dehazing method, named feedback spatial attention dehazing network (FSAD-Net). FSAD-Net is based on the recurrent structure and consists of four modules: a shallow feature extraction block (SFEB), a feedback block (FB), multiple advanced residual blocks (ARBs), and a reconstruction block (RB). FB is designed to handle feedback connections, and it can improve the dehazing performance by exploiting the dependencies of deep features across stages. ARB implements a novel attention-based estimation on a residual block to adapt to pixels with different distributions. Finally, RB helps restore haze-free images. It can be seen from the experimental results that FSAD-Net almost outperforms the state-of-the-arts in terms of five quantitative metrics. Moreover, the qualitatively comparisons on real-world images also demonstrate the superiority of the proposed FSAD-Net. Considering the efficiency and effectiveness of FSAD-Net, it can be expected to serve as a suitable image dehazing baseline in the future.