2D Gaussian Splatting (2DGS) is an emerging explicit scene representation method with significant potential for image compression due to high fidelity and high compression ratios. However, existing low-light enhancement algorithms operate predominantly within the pixel domain. Processing 2DGS-compressed images necessitates a cumbersome decompression-enhancement-recompression pipeline, which compromises efficiency and introduces secondary degradation. To address these limitations, we propose LL-GaussianImage, the first zero-shot unsupervised framework designed for low-light enhancement directly within the 2DGS compressed representation domain. Three primary advantages are offered by this framework. First, a semantic-guided Mixture-of-Experts enhancement framework is designed. Dynamic adaptive transformations are applied to the sparse attribute space of 2DGS using rendered images as guidance to enable compression-as-enhancement without full decompression to a pixel grid. Second, a multi-objective collaborative loss function system is established to strictly constrain smoothness and fidelity during enhancement, suppressing artifacts while improving visual quality. Third, a two-stage optimization process is utilized to achieve reconstruction-as-enhancement. The accuracy of the base representation is ensured through single-scale reconstruction and network robustness is enhanced. High-quality enhancement of low-light images is achieved while high compression ratios are maintained. The feasibility and superiority of the paradigm for direct processing within the compressed representation domain are validated through experimental results.

翻译：2D高斯泼溅（2DGS）是一种新兴的显式场景表示方法，因其高保真度和高压缩比而在图像压缩领域展现出巨大潜力。然而，现有的低光增强算法主要在像素域内进行操作。处理2DGS压缩图像需要繁琐的解码-增强-再编码流程，这不仅降低了效率，还会引入二次质量损失。为克服这些局限性，我们提出了LL-GaussianImage，这是首个专为在2DGS压缩表示域内直接进行低光增强而设计的零样本无监督框架。该框架具备三大核心优势：首先，设计了语义引导的专家混合增强框架。利用渲染图像作为指导，对2DGS的稀疏属性空间施加动态自适应变换，从而实现在无需完全解码至像素网格的情况下完成“压缩即增强”。其次，建立了多目标协同损失函数体系，在增强过程中严格约束平滑度与保真度，在提升视觉质量的同时有效抑制伪影。第三，采用两阶段优化流程实现“重建即增强”。通过单尺度重建确保基础表示的准确性，并增强网络鲁棒性。该方法在保持高压缩率的同时，实现了低光图像的高质量增强。实验结果验证了在压缩表示域内直接处理这一范式的可行性与优越性。