LL-GaussianMap：通过2D高斯散射引导的增益图实现零样本低光照图像增强
LL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps
AI总结：
LL-GaussianMap通过2D高斯散射引导的增益图生成，实现低光照图像增强的无监督方法，具有高效结构重建和低存储开销的优势。
摘要：
在低光照图像增强领域，视觉质量取得了显著进展。然而，大多数现有方法主要在像素域操作或依赖于隐式特征表示。因此，图像的内在几何结构先验往往被忽视。2D高斯散射（2DGS）作为一种显著的显式场景表示技术，具有优越的结构拟合能力和高效的渲染效率。尽管这些优势，2DGS在低级视觉任务中的应用仍处于未探索阶段。为填补这一空白，LL-GaussianMap被提出作为首个将2DGS纳入低光照图像增强的无监督框架。与传统方法不同，增强任务被公式化为由2DGS基本元素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执行高质量的结构重建。然后，通过高斯散射的光栅化机制，通过创新的统一增强模块渲染数据驱动的增强字典系数。这种设计有效地将2DGS的结构感知能力融入增益图生成，从而在增强过程中保留边缘并抑制伪影。此外，通过无监督学习规避了对配对数据的依赖。实验结果表明，LL-GaussianMap在极低存储开销下实现了优越的增强性能，突显了显式高斯表示在图像增强中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
