Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a result, the intrinsic geometric structural priors of images are often neglected. 2D Gaussian Splatting (2DGS) has emerged as a prominent explicit scene representation technique characterized by superior structural fitting capabilities and high rendering efficiency. Despite these advantages, the utilization of 2DGS in low-level vision tasks remains unexplored. To bridge this gap, LL-GaussianMap is proposed as the first unsupervised framework incorporating 2DGS into low-light image enhancement. Distinct from conventional methodologies, the enhancement task is formulated as a gain map generation process guided by 2DGS primitives. The proposed method comprises two primary stages. First, high-fidelity structural reconstruction is executed utilizing 2DGS. Then, data-driven enhancement dictionary coefficients are rendered via the rasterization mechanism of Gaussian splatting through an innovative unified enhancement module. This design effectively incorporates the structural perception capabilities of 2DGS into gain map generation, thereby preserving edges and suppressing artifacts during enhancement. Additionally, the reliance on paired data is circumvented through unsupervised learning. Experimental results demonstrate that LL-GaussianMap achieves superior enhancement performance with an extremely low storage footprint, highlighting the effectiveness of explicit Gaussian representations for image enhancement.

翻译：在低光照图像增强方面，视觉质量已取得显著进展。然而，现有方法大多在像素域操作或依赖隐式特征表示，导致图像固有的几何结构先验常被忽视。二维高斯溅射（2DGS）作为一种新兴的显式场景表示技术，以其卓越的结构拟合能力与高渲染效率著称。尽管具备这些优势，2DGS在底层视觉任务中的应用尚未被探索。为填补这一空白，本文提出LL-GaussianMap——首个将2DGS融入低光照图像增强的无监督框架。与传统方法不同，本框架将增强任务构建为基于2DGS基元引导的增益图生成过程。所提方法包含两个主要阶段：首先利用2DGS执行高保真结构重建；随后通过创新的统一增强模块，借助高斯溅射的光栅化机制渲染数据驱动的增强字典系数。该设计将2DGS的结构感知能力有效融入增益图生成，从而在增强过程中保持边缘并抑制伪影。此外，通过无监督学习避免了对配对数据的依赖。实验结果表明，LL-GaussianMap能以极低的存储开销实现卓越的增强性能，彰显了显式高斯表示在图像增强中的有效性。