QRetinex-Net: Quaternion-Valued Retinex Decomposition for Low-Level Computer Vision Applications

2025-07-22

AI Summary

Low-light images commonly suffer from color shifts, low contrast, and noise corruption, degrading performance in downstream vision tasks. To address four fundamental limitations of classical Retinex models—channel-wise independence, lack of neuroscientific grounding, irreversible reconstruction, and inability to explain color constancy—this paper proposes the first quaternion-based Retinex model. It employs quaternions to jointly represent RGB channels and models reflectance–illumination coupling via Hamiltonian multiplication. A novel reflectance consistency metric is introduced to quantify color constancy. Furthermore, a dedicated quaternion neural network enables end-to-end invertible decomposition. Evaluated on low-light crack detection, multi-illumination face detection, and infrared–visible image fusion, the method outperforms state-of-the-art approaches by 2–11%, achieving significant improvements in color fidelity, noise suppression, and reflectance stability.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Images taken in low light often show color shift, low contrast, noise, and other artifacts that hurt computer-vision accuracy. Retinex theory addresses this by viewing an image S as the pixel-wise product of reflectance R and illumination I, mirroring the way people perceive stable object colors under changing light. The decomposition is ill-posed, and classic Retinex models have four key flaws: (i) they treat the red, green, and blue channels independently; (ii) they lack a neuroscientific model of color vision; (iii) they cannot perfectly rebuild the input image; and (iv) they do not explain human color constancy. We introduce the first Quaternion Retinex formulation, in which the scene is written as the Hamilton product of quaternion-valued reflectance and illumination. To gauge how well reflectance stays invariant, we propose the Reflectance Consistency Index. Tests on low-light crack inspection, face detection under varied lighting, and infrared-visible fusion show gains of 2-11 percent over leading methods, with better color fidelity, lower noise, and higher reflectance stability.

Problem

• Addresses color shift and artifacts in low-light images
• Improves Retinex decomposition with quaternion-valued formulation
• Enhances color fidelity and reflectance stability in vision tasks

Innovation

• Quaternion Retinex formulation for image decomposition
• Reflectance Consistency Index for invariance measurement
• Improved color fidelity and noise reduction in low-light