Towards Perfection: Building Inter-component Mutual Correction for Retinex-based Low-light Image Enhancement

2025-08-12

AI Summary

A key challenge in Retinex-based low-light enhancement is the incomplete decoupling of illumination and reflectance components, leading to residual inter-component residuals (ICRs) that cause perceptual distortion. To address this, we propose IRetinex—a co-optimized decomposition-and-enhancement framework featuring mutual correction. For the first time, IRetinex explicitly models and suppresses ICRs via a cross-component residual suppression module and a feature-similarity-guided residual correction mechanism, enabling joint illumination-reflectance disentanglement and fidelity-preserving enhancement within a deep learning framework. Extensive experiments on three benchmark low-light datasets—LOL, MIT-Adobe FiveK, and SID—demonstrate that IRetinex consistently outperforms state-of-the-art methods. Quantitative evaluations show significant improvements across PSNR, SSIM, and LPIPS metrics, while qualitative results confirm enhanced visual quality and superior detail restoration.

Technology Category

• Computer Vision: Image and Video Retrieval
• Machine Learning: Calibration & Uncertainty Quantification
• 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

In low-light image enhancement, Retinex-based deep learning methods have garnered significant attention due to their exceptional interpretability. These methods decompose images into mutually independent illumination and reflectance components, allows each component to be enhanced separately. In fact, achieving perfect decomposition of illumination and reflectance components proves to be quite challenging, with some residuals still existing after decomposition. In this paper, we formally name these residuals as inter-component residuals (ICR), which has been largely underestimated by previous methods. In our investigation, ICR not only affects the accuracy of the decomposition but also causes enhanced components to deviate from the ideal outcome, ultimately reducing the final synthesized image quality. To address this issue, we propose a novel Inter-correction Retinex model (IRetinex) to alleviate ICR during the decomposition and enhancement stage. In the decomposition stage, we leverage inter-component residual reduction module to reduce the feature similarity between illumination and reflectance components. In the enhancement stage, we utilize the feature similarity between the two components to detect and mitigate the impact of ICR within each enhancement unit. Extensive experiments on three low-light benchmark datasets demonstrated that by reducing ICR, our method outperforms state-of-the-art approaches both qualitatively and quantitatively.

Problem

• Addressing inter-component residuals in Retinex decomposition
• Reducing feature similarity between illumination and reflectance
• Improving low-light image enhancement quality

Innovation

• Inter-correction Retinex model reduces decomposition residuals
• Feature similarity minimizes inter-component residual impact
• Residual reduction module enhances illumination and reflectance separately