Aiming at the existing image dehazing algorithms which still have problems such as incomplete dehazing, blurred edges of dehazed images, and detail information loss, this study presents an image dehazing algorithm based on Transformer and gated fusion mechanism. Global features of the image are extracted by the improved channel self-attention mechanism to improve the efficiency of the model in processing images. A multi-scale gated fusion block is designed to capture features of different scales. The gated fusion mechanism improves the adaptability of the model to different degrees of dehazing by dynamically adjusting weights while better preserving the image edges and detail information. Residual connections are used to enhance the reusability of features and improve the generalization ability of the model. Experimental verification shows that the proposed dehazing algorithm can effectively restore the content information in real hazy images. On the synthesized hazy image dataset SOTS, the peak signal-to-noise ratio reaches 34.841 dB, and the structural similarity reaches 0.984. The dehazed image has complete content information without blurred detail information and incomplete dehazing.