Abstract
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.
Abstract (translated)
在低光图像增强方面，视觉质量有了显著的进步。然而，大多数现有的方法主要是在像素领域操作或依赖隐式的特征表示。因此，通常会忽略图像的内在几何结构先验。二维高斯点阵（2DGS）作为一种显式场景表示技术脱颖而出，其特点是具有出色的结构拟合能力和高效的渲染效率。尽管有这些优点，但将2DGS应用于低级视觉任务的研究尚未进行。 为了填补这一空白，提出了LL-GaussianMap——第一个将2DGS集成到低光图像增强中的无监督框架。不同于传统的方法，增强任务被定义为一个由2DGS原语引导的增益图生成过程。所提出的方法包括两个主要阶段：首先利用2DGS执行高保真结构重建；然后通过新颖的统一增强模块和高斯点阵的光栅化机制渲染数据驱动的增强字典系数。这种设计有效地将2DGS的结构感知能力整合到增益图生成中，在图像增强过程中保持边缘并抑制伪影。 此外，该方法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在存储占用极低的情况下达到了卓越的增强性能，这突显了明确高斯表示法对于图像增强的有效性。
URL
https://arxiv.org/abs/2601.15766
