This study presents a nonuniform dehazing method based on hierarchical weight interaction and Laplacian prior to address the issues of detail loss and residual haze in nonuniform hazy images, which often result in degraded image quality. First, a hierarchical weight interaction module is introduced in the baseline network to adaptively adjust weights and perform a weighted fusion of feature maps at different scales. Furthermore, a global receptive field aggregation module is introduced to enrich the receptive field, allowing the model to comprehensively understand the content information in the image. Then, a frequency domain information branch is introduced to decompose the image into low-frequency and high-frequency components using wavelet functions. The low-frequency component contains global structural information, whereas the high-frequency component provides detailed local information. This decomposition collectively enhances the image clarity. Finally, a Laplacian loss is incorporated to reconstruct the image, effectively restoring its fine-grained features and improving the quality of the generated images. Experimental results show that the proposed algorithm achieves superior results on the test set, with an increase in peak signal-to-noise ratio (PSNR) by 0.8 dB, 1.54 dB, 1.14 dB, and 0.23 dB compared with the original algorithm on four datasets.