IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition

2025-11-04

AI Summary

To address illumination non-uniformity, noise corruption, and brightness-color imbalance in low-light images, this paper proposes a joint enhancement framework integrating Retinex decomposition with a Conditional Rectification Flow (CRF). Methodologically, the approach first decomposes an image into illumination and reflectance components via a Retinex model; second, it employs CRF to explicitly model the continuous spatial variation of illumination for adaptive lighting adjustment; third, it introduces a flow-field-driven data augmentation strategy to jointly train a deep denoising network, enabling simultaneous noise suppression in the reflectance domain and customizable brightness enhancement. Extensive experiments demonstrate that the method achieves state-of-the-art performance on multiple low-light enhancement and exposure correction benchmarks, yielding consistent improvements in quantitative metrics (PSNR/SSIM) and visual quality. The framework exhibits strong robustness to diverse degradation patterns and maintains high practicality for real-world deployment.

Technology Category

• Computer Vision: Learning & Optimization for CV
• 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
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We present IllumFlow, a novel framework that synergizes conditional Rectified Flow (CRF) with Retinex theory for low-light image enhancement (LLIE). Our model addresses low-light enhancement through separate optimization of illumination and reflectance components, effectively handling both lighting variations and noise. Specifically, we first decompose an input image into reflectance and illumination components following Retinex theory. To model the wide dynamic range of illumination variations in low-light images, we propose a conditional rectified flow framework that represents illumination changes as a continuous flow field. While complex noise primarily resides in the reflectance component, we introduce a denoising network, enhanced by flow-derived data augmentation, to remove reflectance noise and chromatic aberration while preserving color fidelity. IllumFlow enables precise illumination adaptation across lighting conditions while naturally supporting customizable brightness enhancement. Extensive experiments on low-light enhancement and exposure correction demonstrate superior quantitative and qualitative performance over existing methods.

Problem

• Enhancing low-light images by separating illumination and reflectance components
• Modeling illumination variations through conditional rectified flow framework
• Removing noise from reflectance while preserving color fidelity

Innovation

• Combines conditional rectified flow with Retinex decomposition
• Separately optimizes illumination and reflectance components
• Uses flow-enhanced denoising network for noise removal