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

2026-04-11

AI Summary

This work addresses the challenges of deploying low-light image enhancement models—namely, excessive model size, reliance on a single color space, and resulting color or exposure artifacts—by proposing an ultra-lightweight structured framework. Built upon Retinex-based residual decomposition, the method uniquely integrates multi-color-space priors within a lightweight architecture, separately optimizing illumination and reflectance components to achieve exposure correction, with an emphasis on enhancement rather than reconstruction. By leveraging multi-prior representations, lightweight neural operations, and multi-representation fusion, the authors construct an efficient network containing only 0.7K–45K parameters. Extensive experiments demonstrate that the proposed approach surpasses existing lightweight state-of-the-art methods across multiple benchmarks and achieves performance comparable to much heavier models, effectively balancing computational efficiency and visual enhancement quality.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Low-light image enhancement
• Edge deployment
• Color artifacts
• Computational efficiency
• Illumination degradation

Innovation

• Low-light image enhancement
• Multi-prior Retinex
• Lightweight neural network
• Color fidelity
• Illumination decomposition