将Retinex模型改到直方图域，实现快速低光图像增强。
HistRetinex: Optimizing Retinex model in Histogram Domain for Efficient Low-Light Image Enhancement
- 在直方图域构建新Retinex模型，通过双层优化求解光照与反射直方图。
- 处理1000*664图像仅需1.86秒，比现有方法快至少6.67秒。
- 适合需要实时低光增强的场景，如手机摄影、监控系统。
基于Retinex的低光图像增强方法因效果优异而广泛应用，但多数方法对大尺寸图像处理速度较慢。本文将Retinex模型从空间域扩展至直方图域，提出一种新的基于直方图的Retinex模型——HistRetinex。首先定义直方图位置矩阵和直方图计数矩阵，建立光照、反射与低光图像直方图之间的关联关系；其次，结合先验信息与直方图模型，构建新颖的两层优化框架，推导出光照直方图与反射直方图的迭代公式；最后通过匹配目标图像与HistRetinex生成的直方图实现增强。实验表明，HistRetinex在可视性与性能指标上均优于现有方法，且在1000×664分辨率图像上仅耗时1.86秒，最快比现有方法节省6.67秒。
原文摘要 · Abstract (English)
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.
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