Existing single image dehazing methods are often constrained by computational redundancy in pixel-level optimization and the lack of physical interpretability in implicit neural networks. These limitations hinder the balance between representation efficiency and reconstruction fidelity. To address these issues, we propose Dehaze-GaussianImage, the first zero-shot framework that introduces 2D Gaussian Splatting (2DGS) into the image dehazing domain to break the traditional pixel-grid processing paradigm. Distinct from static convolutional neural networks (CNNs) or Transformers, our approach models hazy images as continuous and dynamically evolvable anisotropic Gaussian fields. Specifically, we propose a novel reconstruction-decoupling zero-shot learning strategy that embeds the atmospheric scattering model into the Gaussian parameter space. This strategy drives Gaussian primitives to adaptively split, clone, and prune during optimization, achieving geometric-level decoupling of the transmission medium and clear textures. Furthermore, explicit structure-preserving constraints are introduced to suppress artifacts commonly caused by traditional physical priors. Experimental results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in a fully unsupervised manner with minimal parameters, highlighting the potential of explicit Gaussian representation for low-level vision tasks.

翻译：现有单图像去雾方法常受限于像素级优化中的计算冗余以及隐式神经网络缺乏物理可解释性。这些局限阻碍了表示效率与重建保真度之间的平衡。为解决上述问题，我们提出Dehaze-GaussianImage，这是首个将二维高斯溅射（2DGS）引入图像去雾领域的零样本框架，突破了传统像素网格处理范式。与静态卷积神经网络（CNN）或Transformer不同，该方法将有雾图像建模为连续且动态可演化各向异性高斯场。具体而言，我们提出一种新颖的重建-解耦零样本学习策略，将大气散射模型嵌入高斯参数空间。该策略驱动高斯基元在优化过程中自适应分裂、克隆与剪枝，实现传输介质与清晰纹理的几何级解耦。此外，引入显式结构保持约束以抑制传统物理先验常引发的伪影。实验表明，所提方法在完全无监督模式下以极简参数量达到最先进（SOTA）性能，凸显了显式高斯表示在低级视觉任务中的潜力。