Dense and non-uniform haze in real-world scenes exhibits complex spatial variations, which significantly degrades the generalization ability of existing deep image dehazing models, leading to unstable detail recovery and high deployment costs. To address these challenges, this paper proposes a dual-path structurally re-parameterized dehazing network (re-parameterized large kernel dehaze, RepLKDehaze). The proposed framework consists of a large-kernel structural re-parameterization subnet (large-kernel rep subnet, RepLK) and a full-resolution attention subnet (full-resolution attention subnet, FRA). The former is built upon a trimmed UniRepLKNet-s encoder-decoder backbone, where multi-branch representations are introduced via dilated re-parameterization during training and equivalently merged into a single large-kernel convolution at inference, enabling efficient modeling of global haze distribution. The latter operates at full spatial resolution and incorporates residual channel attention to compensate for local texture and edge information loss caused by downsampling. By fusing features from both paths, RepLKDehaze achieves a balanced reconstruction between global consistency and fine-grained detail preservation. Experimental results demonstrate that although RepLKDehaze does not achieve the best performance across all metrics on synthetic datasets such as RESIDE-SOTS, it consistently outperforms state-of-the-art methods on real-world datasets that better reflect realistic imaging degradation. Specifically, it achieves 17.66 dB/0.613/0.494 (PSNR/SSIM/LPIPS) on Dense-Haze, 21.66 dB/0.716/0.254 on NH-Haze 2020, and 22.74 dB/0.857/0.169 on NH-Haze 2021. The proposed method shows clear advantages in color fidelity, structural preservation, and residual haze suppression. These results indicate that the proposed dual-path structurally re-parameterized dehazing network effectively enhances robustness in real dense non-uniform haze scenarios while maintaining deployment efficiency, providing a practical solution for real-world hazy-scene visual perception tasks.