Existing low-light image enhancement methods often face a bottleneck between the representation capacity of illumination-field modeling and computational complexity. To address this issue, this paper proposes an Adaptive Illumination Gaussian Splatting Network (AIGS-Net), an ultra-lightweight architecture for fast low-light enhancement. Unlike conventional static priors, AIGS-Net constructs an input-adaptive 2D Gaussian Splatting illumination field. The opacity of Gaussian basis functions is dynamically modulated by relative luminance statistics of the input image, and spatially varying illumination compensation is rendered through ordered alpha compositing. To guide adaptive illumination compensation efficiently, a zero-parameter nonlinear multiscale contextual encoding module is introduced to extract low-frequency structures and local contrast cues without additional convolutional weights. To suppress noise amplification and sensor-induced color bias, AIGS-Net integrates noise-mask estimation, locked single-channel Gamma mapping, cross-channel consistency regularization, and target color-alignment constraints. Experiments on LOL and LSRW benchmarks show that AIGS-Net improves detail recovery and color fidelity while requiring only approximately 40 learnable parameters, achieving an effective trade-off between enhancement quality and extreme inference efficiency.

翻译：现有低光照图像增强方法往往面临光照场建模表示能力与计算复杂度之间的瓶颈。针对这一问题，本文提出自适应光照高斯泼溅网络（AIGS-Net），一种用于快速低光照增强的超轻量级架构。与传统的静态先验不同，AIGS-Net构建了输入自适应的二维高斯泼溅光照场。高斯基函数的不透明度由输入图像的相对亮度统计动态调节，并通过有序阿尔法混合渲染空间变化的光照补偿。为了高效引导自适应光照补偿，引入了一个零参数非线性多尺度上下文编码模块，无需额外卷积权重即可提取低频结构与局部对比度线索。为抑制噪声放大和传感器引起的色偏，AIGS-Net整合了噪声掩膜估计、锁定单通道伽马映射、跨通道一致性正则化及目标颜色对齐约束。在LOL和LSRW基准上的实验表明，AIGS-Net在仅需约40个可学习参数的情况下，提升了细节恢复与色彩保真度，实现了增强质量与极端推理效率之间的有效平衡。