Existing unsupervised low-light image enhancement methods often encounter local exposure imbalance and color distortion under complex non-uniform illumination. In addition, most Vision Transformers lack an explicit mechanism for modeling the physical priors of illumination degradation. To address these limitations, we propose GLFS, a Gaussian light field splatting-based Vision Transformer that integrates continuous physical illumination modeling from Gaussian splatting into the Transformer architecture. In GLFS, scene illumination is represented by a superposition of anisotropic Gaussian basis functions. Physics-guided biases are introduced into self-attention to adaptively infer a spatial gain field, enabling accurate and uniform restoration under complex illumination. To reduce color bias and structural degradation during enhancement, a color-vector angular loss and a luminance-edge loss are further developed. These losses enforce hue consistency and improve the structural fidelity of local details. Extensive ablation studies and quantitative evaluations show that GLFS provides clear advantages in illumination correction and detail preservation. It achieves state-of-the-art performance and offers a new representation paradigm for low-light image enhancement.

翻译：现有无监督低光照图像增强方法在复杂非均匀光照下常出现局部曝光失衡和色彩失真问题。此外，大多数视觉Transformer缺乏对光照退化的物理先验进行显式建模的机制。针对这些局限，我们提出GLFS——一种基于高斯光场散射的视觉Transformer，将高斯散射的连续物理光照建模融入Transformer架构。在GLFS中，场景光照通过各向异性高斯基函数的叠加来表示。在自注意力机制中引入物理引导偏置，自适应推断空间增益场，使复杂光照下能实现准确均匀的恢复。为减少增强过程中的色偏和结构退化，进一步开发了色向量角损失和亮度边缘损失。这些损失函数能强化色调一致性并提升局部细节的结构保真度。大量消融实验和定量评估表明，GLFS在光照校正和细节保持方面具有显著优势。该方法达到了最先进性能，并为低光照图像增强提供了新的表征范式。