HistRetinex: Optimizing Retinex model in Histogram Domain for Efficient Low-Light Image Enhancement

2025-10-23

AI Summary

To address the high computational cost of conventional Retinex methods for large-scale image enhancement, this paper pioneers the migration of the Retinex model into the histogram domain, establishing explicit histogram-level mappings between illumination and reflectance components. We propose a two-stage histogram-domain optimization framework: first, modeling via a position-count matrix; second, deriving closed-form iterative update rules for illumination and reflectance by integrating domain-specific priors, followed by efficient histogram matching for enhancement. Evaluated on a 1000×664 image, our method completes in only 1.86 seconds—accelerating over state-of-the-art approaches by at least 6.67 seconds—while achieving significant improvements in PSNR, SSIM, and perceptual quality. The core contribution lies in establishing the first histogram-domain Retinex modeling paradigm, uniquely balancing computational efficiency, reconstruction accuracy, and interpretability.

Technology Category

• Computer Vision: Image and Video Retrieval
• Search and Optimization: Non-convex Optimization
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• 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

Retinex-based low-light image enhancement methods are widely used due to their excellent performance. However, most of them are time-consuming for large-sized images. This paper extends the Retinex model from the spatial domain to the histogram domain, and proposes a novel histogram-based Retinex model for fast low-light image enhancement, named HistRetinex. Firstly, we define the histogram location matrix and the histogram count matrix, which establish the relationship among histograms of the illumination, reflectance and the low-light image. Secondly, based on the prior information and the histogram-based Retinex model, we construct a novel two-level optimization model. Through solving the optimization model, we give the iterative formulas of the illumination histogram and the reflectance histogram, respectively. Finally, we enhance the low-light image through matching its histogram with the one provided by HistRetinex. Experimental results demonstrate that the HistRetinex outperforms existing enhancement methods in both visibility and performance metrics, while executing 1.86 seconds on 1000*664 resolution images, achieving a minimum time saving of 6.67 seconds.

Problem

• Optimizing Retinex model in histogram domain for efficiency
• Reducing computational time for large-sized low-light images
• Enhancing image visibility while improving performance metrics

Innovation

• Extends Retinex model to histogram domain
• Constructs two-level optimization using histogram matrices
• Enhances images via histogram matching with iterative formulas