Abstract
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.
Abstract (translated)
2D高斯点阵（2DGS）是一种新兴的显式场景表示方法，由于其高保真度和高压缩比，在图像压缩方面具有巨大的潜力。然而，现有的低光增强算法主要在像素域内操作。处理通过2DGS压缩的图像需要一个复杂的解压-增强-重新压缩管道流程，这不仅效率低下，还会引入二次退化问题。为了克服这些限制，我们提出了LL-GaussianImage框架，这是首个针对低光图像直接在2DGS压缩表示域内进行无监督零样本增强处理的方法。 该框架提供了三个主要优势： 1. 设计了一个基于语义引导的专家混合增强框架。通过使用渲染图像作为指导，在不完全解压到像素网格的情况下对2DGS的稀疏属性空间应用动态自适应变换，从而实现压缩与增强一体化的效果。 2. 建立了一种多目标协作损失函数系统，严格限制了在增强过程中保持平滑度和保真度的要求。这种方法不仅可以抑制伪影，还可以提高视觉质量。 3. 采用两阶段优化过程来实现重建即增强的目标。通过单尺度重建确保基础表示的准确性，并加强网络鲁棒性，在维持高压缩比的同时实现了低光图像的高质量增强效果。 实验结果验证了直接在压缩表示域内进行处理的可行性和优越性，展示了LL-GaussianImage框架的有效性。
URL
https://arxiv.org/abs/2601.15772
