Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE techniques often rely on large models and multi-stage training, limiting practicality for edge deployment. Moreover, their dependence on a single color space introduces instability and visible exposure or color artifacts. To address these, we propose Multinex, an ultra-lightweight structured framework that integrates multiple fine-grained representations within a principled Retinex residual formulation. It decomposes an image into illumination and color prior stacks derived from distinct analytic representations, and learns to fuse these representations into luminance and reflectance adjustments required to correct exposure. By prioritizing enhancement over reconstruction and exploiting lightweight neural operations, Multinex significantly reduces computational cost, exemplified by its lightweight (45K parameters) and nano (0.7K parameters) versions. Extensive benchmarks show that all lightweight variants significantly outperform their corresponding lightweight SOTA models, and reach comparable performance to heavy models. Paper page available at https://albrateanu.github.io/multinex.

翻译：低光图像增强旨在恢复严重光照退化下的自然可见性、色彩保真度及结构细节。当前最先进的低光增强技术通常依赖大模型与多阶段训练，限制了在边缘设备上的实用性。此外，其对单一色彩空间的依赖会导致不稳定性及可见的曝光或色彩伪影。为解决这些问题，我们提出了一种超轻量级结构化框架Multinex，该框架在基于Retinex残差建模的框架内集成多种细粒度表征。它将图像分解为源自不同解析表示的照明与色彩先验堆栈，并学习将这些表征融合为校正曝光所需的亮度与反射率调整。通过优先增强而非重建并利用轻量级神经运算，Multinex显著降低了计算成本，其轻量版（4.5万参数）与纳版（0.07万参数）即为明证。大量基准测试表明，所有轻量变体显著优于对应的轻量级最先进模型，并达到与重型模型相当的性能。论文页面详见https://albrateanu.github.io/multinex。