High-dynamic range (HDR) image reconstruction algorithms based on the generation of bracketed image stacks have gained popularity for their capabilities in expanding the dynamic range and adapting to complex lighting scenarios. However, existing approaches based on convolutional neural networks often suffer from local receptive fields, limiting the utilization of global information and recovery of over- or underexposed regions. To solve this problem, this study introduces a Transformer architecture that equips the network with a global receptive field to establish long-range dependency. In addition, a unidirectional soft mask is added to the Transformer to alleviate the effects of invalid information from over- and underexposed regions, further improving the reconstruction quality. Experimental results show that the proposed algorithm improves the peak signal-to-noise ratio by 2.37 dB and 1.33 dB on the VDS and HDREye datasets, respectively, and subjective comparisons further prove the effectiveness of the proposed algorithm. This study provides a novel approach for improving the information recovery capabilities of HDR image reconstruction algorithms for over- and underexposed regions.