To address the problems of detail loss and color distortion in the current image defogging algorithms, this paper proposes a multi-dimensional attention feature fusion image dehazing algorithm. The core step of the proposed algorithm is the introduction of a union attention mechanism module, which can simultaneously operate in three dimensions of channel, space, and pixel to achieve accurate enhancement of local features, while parallel a multi-scale perceptual feature fusion module effectively captures global feature information of different scales. To achieve a more refined and accurate dehazing effect, a bi-directional gated feature fusion mechanism is added to the proposed algorithm to realize the deep fusion and complementarity of local information and global information features. Experimental validation on multiple datasets, such as RESIDE, I-Hazy, and O-Hazy shows that, the proposed algorithm exhibits better performance than the existing state-of-the-art in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with the classical GCA-Net, the PSNR and SSIM of the proposed algorithm increased by 2.77 dB and 0.0046, respectively. Results of this study can provide new insights and directions for investigating image dehazing algorithms.