轻量级低光图像增强，多先验Retinex提升清晰度与色彩稳定性。
Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex
- 基于多先验的Retinex残差框架，分离光照与颜色信息
- 45K参数版本性能超越同类轻量模型，接近大模型表现
- 适合边缘设备部署，有效避免过曝和色偏问题
低光图像增强（LLIE）旨在恢复严重光照退化下的自然可视性、色彩保真度和结构细节。现有先进方法多依赖大模型与多阶段训练，限制了边缘部署的实用性；且单一色彩空间易引发不稳定性和曝光或色彩伪影。为此，我们提出Multinex，一种超轻量级结构化框架，通过在原理化的Retinex残差形式中整合多个细粒度表示，将图像分解为源自不同分析表示的光照与颜色先验堆栈，并学习融合这些表示以生成亮度与反射率修正。该方法优先考虑增强而非重建，结合轻量神经操作，显著降低计算开销：其轻量版仅45K参数，纳米版仅0.7K参数。大量基准测试显示，所有轻量变体均显著优于对应轻量级SOTA模型，并达到与重型模型相当的性能。
原文摘要 · Abstract (English)
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.
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