Convolution operations with parameter sharing features primarily focus on the extraction of local features of images but fail to model the features beyond the range of the receptive field. Moreover， when the parameters of an entire image share the same convolution kernel， the characteristics of different regions are ignored. To address this limitation in existing methods， a global and local feature fusion dehazing network is proposed. We utilize transformer and convolution operations to extract global and local feature information from images， respectively. Subsequently， we merge and output these features， effectively employing the advantages of transformers in modeling long-distance dependencies and the local perception of convolution operations， thus achieving efficient feature expression. Before the final output of restored images， we incorporate an enhancement module that includes multi-scale patches to further aggregate global feature information and enhance the details of the restored images using a transformer. Simultaneously， we introduce a global positional encoding generator， which can adaptively generate positional encodings based on the global content information of images， thereby enabling 2D spatial location modeling of the dependency relationship between pixels. Experimental results demonstrate the superior performance of the proposed dehazing network on both synthetic and real image datasets， producing more realistic restored images and significantly reducing detail loss.