Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

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 depend on large model size and multi-stage training, limiting practicality for edge deployment. Moreover, they often rely on a single color space, which introduces instability and visible exposure or color artifacts. To achieve low-cost, effective LLIE, we present Multinex, an ultra-lightweight structured framework that integrates multiple fine-grained representations within a principled Retinex 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. We emphasize enhancement over reconstruction to enable drastic reduction of computational overhead, supported by lightweight neural operations. Accordingly, we develop a lightweight Multinex (45K parameters) and a micro version (2.6K parameters). Examined by intensive benchmark comparison, they outperform significantly lightweight and micro SOTA models, while reach close performance to large SOTA models. Code will be released upon publication. Across extensive benchmarks, both variants significantly outperform existing lightweight and micro SOTA models, and reach performance comparable to more complex approaches. Code will be released upon publication.